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Agriculture
Volume 13
Issue 11
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Agriculture, Volume 13, Issue 11 (November 2023) – 133 articles

Cover Story (view full-size image): The life cycle assessment is an essential tool for evaluating the environmental loads in an agriculture system. This study indicated that the environmental performance of wheat production could be greatly improved by shifting from conventional chemical fertilizers to more environmentally friendly organic farming systems. Reducing the environmental load produced within the cultivation of winter wheat can be achieved by reducing the dose of fertilizers at the cost of a lower yield. The excessive use of N fertilizer has an increasing environmental impact. The reduction in the amount of GHG produced within the cultivation could also be reduced using different cultivation technology. View this paper
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18 pages, 13099 KiB  
Article
Design and Experiment of Uniform Seed Device for Wide-Width Seeder of Wheat after Rice Stubble
by Weiwen Luo, Xulei Chen, Mingyang Qin, Kai Guo, Jie Ling, Fengwei Gu and Zhichao Hu
Agriculture 2023, 13(11), 2173; https://doi.org/10.3390/agriculture13112173 - 20 Nov 2023
Cited by 1 | Viewed by 1542
Abstract
When wide-width sowing wheat after rice stubble (WRS) in a rice-wheat rotation area, there is a problem of poor uniform of seed distribution. To solve the problem, this study designed the seed distribution plate (SDP) structure and optimized its critical structure parameters. Firstly, [...] Read more.
When wide-width sowing wheat after rice stubble (WRS) in a rice-wheat rotation area, there is a problem of poor uniform of seed distribution. To solve the problem, this study designed the seed distribution plate (SDP) structure and optimized its critical structure parameters. Firstly, combined with the operating principles of the wide-width seeder and the agricultural standards for WRS, the main structural parameters affecting seed movement were determined by a theoretical analysis of seed grain dynamics and SDP structure. Secondly, the operational performance of six different structures of SDP under different structural parameters was compared using discrete element simulation technology. The structure of SDP most suitable for WRS wide-width seeding and the value ranges of key structural parameters that have a significant impact on the coefficient of the variation of seed lateral uniformity (CVLU) were determined. Finally, the pattern and mechanism of the influence of key structural parameters of SDP on the CVLU were analyzed. The optimum parameter combination was obtained and a field validation test was conducted on this. The results showed that the anti-arc ridge and arc bottom structure (S6) is more suitable for the agronomy standards of WRS wide-width seeding. The chord length of ridge, installation inclination, angle between the chord and tangent of the end of ridge line (ACT), span, and bottom curve radius are determined as the key structural parameters affecting the CVLU, and there is a lower CVLU (42.8%) when the ACT is 13°. The primary and secondary order of the influence of each factor on CVLU is the chord length of the ridge, span, installation inclination, and bottom curve radius. The corresponding parameter values after optimization are 140 mm, 40°, 75 mm and 50 mm, respectively. A field test was conducted on the SDP after optimizing parameters, and the CVLU was 30.27%, which was significantly lower than the CVLU before optimization. Full article
(This article belongs to the Special Issue Advances in Modern Agricultural Machinery)
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<p>Operation effect of technique with straw inter-row mulching and wide-width sowing WRS: (<b>a</b>) untreated rice straw land after rice harvest; (<b>b</b>) field after operation.</p> Full article ">Figure 2
<p>The overall structure of a wide-width planter with crushed straw inter-row mulching: 1. Straw-crushing device; 2. straw diversion device; 3. seed strip rotary tillage device; 4. seed uniform distribution device with wide width; 5. press wheel.</p> Full article ">Figure 3
<p>Structure of seed uniform distribution device with wide width.</p> Full article ">Figure 4
<p>Mathematical structural model of the seed distribution plate.</p> Full article ">Figure 5
<p>Using EDEM software post-processing module for data statistics: (<b>a</b>) DEM model of wheat; (<b>b</b>) test and statistical method of CVLU.</p> Full article ">Figure 6
<p>Schematic diagram of data statistics in the sampling area.</p> Full article ">Figure 7
<p>Performance verification test of SDP: (<b>a</b>) bench test to verify the accuracy of simulation results; (<b>b</b>) field performance verification test.</p> Full article ">Figure 8
<p>The effect of ridge parameters on CVLU: (<b>a</b>) the effect of the chord length of the ridge on CVLU; (<b>b</b>) the effect of installation inclination on CVLU; (<b>c</b>) the effect of ACT on CVLU.</p> Full article ">Figure 9
<p>The effect of ridge parameters on CVLU: (<b>a</b>) the effect of span on CVLU; (<b>b</b>) the effect of bottom transverse radius on CVLU.</p> Full article ">Figure 10
<p>Effect of interaction between factors on CVLU. (<b>a</b>) Y = f (A, B, 0, 0); (<b>b</b>) Y = f (A, 0, C, 0); (<b>c</b>) Y = f (0, B, C, 0); (<b>d</b>) Y = f (0, 0, C, D).</p> Full article ">Figure 11
<p>Data statistics of field trials.</p> Full article ">Figure 12
<p>Statistical results of actual verification tests: (<b>a</b>) Comparison of the results of CVLU at different levels; (<b>b</b>) comparison of the results of sowing depth at different levels. Note: Q1, Q2, and Q3 mean 180, 225, and 270 kg/hm<sup>2</sup>, respectively.</p> Full article ">
13 pages, 1339 KiB  
Article
Biochar and Cd Alter the Degradation and Transport of Kasugamycin in Soil and Spinach
by Liqiang Cui, Jingwen Ma, Guixiang Quan, Jinlong Yan, James A. Ippolito and Hui Wang
Agriculture 2023, 13(11), 2172; https://doi.org/10.3390/agriculture13112172 - 20 Nov 2023
Cited by 2 | Viewed by 1398
Abstract
Biochar has been widely studied to reduce multiple contaminant sources in one matrix (e.g., several heavy metals in soils), yet less attention has been paid to accelerating pesticide degradation while in the presence of any heavy metals, such as when kasugamycin (KSM) and [...] Read more.
Biochar has been widely studied to reduce multiple contaminant sources in one matrix (e.g., several heavy metals in soils), yet less attention has been paid to accelerating pesticide degradation while in the presence of any heavy metals, such as when kasugamycin (KSM) and cadmium (Cd) are both present in soil. While KSM has low toxicity compared to other pesticides, it can be a potential health risk when applied to vegetable crops, especially when KSM is used or overapplied to achieve rapid reductions in insect and disease pressure. The degradation behavior of KSM (2 kg ha−1) in the presence of Cd (20 mg kg−1) and biochar (5% by wt.) when growing spinach (Spinacia oleracea) was studied. The biochar increased spinach shoot and root biomass by 51.0–54.8% and 24.4–39.0%, respectively, compared to the KSM treatment only. Compared to the treatments that did not receive biochar, the biochar application increased the KSM degradation in the soil by 8.4–68.4% and, subsequently, less KSM was absorbed by the spinach roots (18.0–48.4%) and shoots (33.0–33.2%). The KSM degradation rate, as a function of soil depth, was enhanced in the presence of Cd. The biochar also effectively decreased the KSM concentration with soil depth, reducing downward KSM migration. The KSM degradation, increased by the biochar, led to smaller organic moieties and some macromolecular organic phases. In soils that are contaminated with Cd and where vegetables are raised, biochar may be used as an environmentally friendly proponent for increasing KSM degradation, reducing KSM downward transport and, thus, protecting environmental and human health. Full article
(This article belongs to the Special Issue Agricultural Environmental Pollution, Risk Assessment, and Control)
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Graphical abstract
Full article ">Figure 1
<p>Changes in soil pH and soil organic carbon (SOC). (<b>A</b>) Soil pH (0–5 cm in depth) as a function of time; (<b>B</b>) soil pH as a function of soil depth, obtained at 14 days after KSM application; (<b>C</b>) SOC as a function of time; (<b>D</b>) SOC as a function of soil depth, obtained at 14 days after KSM application. Treatment PZ1 = 100 mg box<sup>−1</sup> was sprayed on spinach leaves in unamended soil; soil containing PZ2 = 20 mg Cd kg<sup>−1</sup> added to an unamended soil surface, and 100 mg box<sup>−1</sup> sprayed on spinach leaves; soil amended with PZ3 = 5% biochar (<span class="html-italic">w</span>:<span class="html-italic">w</span>), and 100 mg box<sup>−1</sup> sprayed on spinach leaves; and soil containing PZ4 = 20 mg Cd kg<sup>−1</sup> added to the surface of a soil amended with 5% biochar (<span class="html-italic">w</span>:<span class="html-italic">w</span>), and 100 mg box<sup>−1</sup> sprayed on spinach leaves. Different lower-case letters above error bars (error bars: the standard deviation of the mean; n = 3; <span class="html-italic">p</span> &lt; 0.05, determined via a Tukey post hoc test) indicate statistically significant differences between treatments within a particular time (<b>A</b>,<b>C</b>) or depth (<b>B</b>,<b>D</b>).</p> Full article ">Figure 2
<p>KSM concentrations in (<b>A</b>) soil (0–5 cm in depth) as a function of time and (<b>B</b>) spinach roots and shoots at the end of the study (28 days after KSM application). Treatment PZ1 = 100 mg box<sup>−1</sup> (equivalent to (2 kg ha<sup>−1</sup>) was sprayed on spinach leaves in unamended soil; soil containing PZ2 = 20 mg Cd kg<sup>−1</sup> added to an unamended soil surface, and 100 mg box<sup>−1</sup> sprayed on spinach leaves; soil amended with PZ3 = 5% biochar (<span class="html-italic">w</span>:<span class="html-italic">w</span>), and 100 mg box<sup>−1</sup> sprayed on spinach leaves; and soil containing PZ4 = 20 mg Cd kg<sup>−1</sup> added to the surface of a soil amended with 5% biochar (<span class="html-italic">w</span>:<span class="html-italic">w</span>), and 100 mg box<sup>−1</sup> sprayed on spinach leaves. Different lower-case letters above error bars (error bars: the standard deviation of the mean; n = 3; <span class="html-italic">p</span> &lt; 0.05, determined via a Tukey post hoc test) indicate statistically significant differences between treatments.</p> Full article ">Figure 3
<p>Soil KSM (<b>A</b>) and Cd (<b>B</b>) concentrations by soil depth at the end of the study (14 days after KSM application). Treatment PZ1 = 100 mg KSM box<sup>−1</sup> (equivalent to (2 kg ha<sup>−1</sup>) was sprayed on spinach leaves in unamended soil; soil containing PZ2 = 20 mg Cd kg<sup>−1</sup> added to an unamended soil surface, and 100 mg box<sup>−1</sup> sprayed on spinach leaves; soil amended with PZ3 = 5% biochar (<span class="html-italic">w</span>:<span class="html-italic">w</span>), and 100 mg box<sup>−1</sup> sprayed on spinach leaves; and soil containing PZ4 = 20 mg Cd kg<sup>−1</sup> added to the surface of a soil amended with 5% biochar (<span class="html-italic">w</span>:<span class="html-italic">w</span>), and 100 mg box<sup>−1</sup> sprayed on spinach leaves. Different lower-case letters above error bars (error bars: the standard deviation of the mean; n = 3; <span class="html-italic">p</span> &lt; 0.05, determined via a Tukey post hoc test) indicate statistically significant differences between treatments within a particular depth.</p> Full article ">Figure 4
<p>FTIR of raw biochar and soil treatment 1 day following KSM application to spinach leaves. Note: PZ1, 100 mg box<sup>−1</sup> (equivalent to (2 kg ha<sup>−1</sup>) was sprayed on spinach leaves in unamended soil; soil amended with PZ3, 5% biochar (<span class="html-italic">w</span>:<span class="html-italic">w</span>), and 100 mg box<sup>−1</sup> sprayed on spinach leaves.</p> Full article ">Figure 5
<p>Principal component analysis as a function of soil properties and Cd presence, plant biomass, and KSM in both soils and plants.</p> Full article ">
20 pages, 3244 KiB  
Article
What Do Cross-Range Germination, Growth, and Interaction Studies Reveal about the Behaviour of an Expansive Plant Species?
by Krishan Kaushik, Robert W. Pal, Katalin Somfalvi-Tóth, Riyazuddin Riyazuddin, Kinga Rudolf and Tamás Morschhauser
Agriculture 2023, 13(11), 2171; https://doi.org/10.3390/agriculture13112171 - 20 Nov 2023
Viewed by 1380
Abstract
Understanding the invasion potential of any plant species is crucial for early detection in habitat conservation, particularly when observing their expansion within their native region. As a test species, we utilised Allium ursinum L., a dominant clonal species in early spring forest floors. [...] Read more.
Understanding the invasion potential of any plant species is crucial for early detection in habitat conservation, particularly when observing their expansion within their native region. As a test species, we utilised Allium ursinum L., a dominant clonal species in early spring forest floors. We compared the species’ germination capacity in native (Hungarian) and non-native (North American) soils, its seedling growth, and competing performances with two co-occurring dominant species, Melica uniflora Retz. and Carex pilosa Scop., in ten soil types and three soil compositions, respectively. Additionally, the competitive interactions of A. ursinum with Convallaria majalis L., a species already introduced in North America, were assessed under three moisture conditions. The results revealed that A. ursinum exhibited enhanced germination in non-native soils, while its shoot growth was most vigorous in control soil. When grown in soils with different co-dominant species, A. ursinum seedlings exhibited varying growth rates, significantly influenced by solar radiation intensity. A. ursinum shoots displayed superior growth in soil collected from C. pilosa stands compared to soil originating from its own stands. Notably, A. ursinum effectively competed against C. majalis in moderate soil moisture conditions. Furthermore, increasing sand content improved the competitive ability of A. ursinum against C. pilosa and M. uniflora. Based on our findings, A. ursinum possesses an invasion potential for particular North American habitats. However, the extent of its potential is dependent upon soil and climatic conditions. Under medium moisture regime, A. ursinum might outcompete the already established C. majalis from its habitats. Additionally, it can potentially displace native species with comparable ecological characteristics, such as C. pilosa and M. uniflora, especially in loose soils. Similar cross-range seed germination, growth, and paired competition experiments with potential competitor species are highly recommended as these can not only elucidate its native range expansion but also various growth scenarios for its agricultural cultivation. Full article
(This article belongs to the Topic Plant-Soil Interactions, 2nd Volume)
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<p>Comparative germination profile of significantly different <span class="html-italic">A. ursinum</span> seeds germination in non-native U.S. soils (in green) against the native Hungarian <span class="html-italic">A. ursinum</span> soils (in brown). Error bar represent the standard error in dataset.</p> Full article ">Figure 2
<p>The mean growth rate of <span class="html-italic">A. ursinum</span> shoots for 21 weeks across different soil types. Legend: AU = <span class="html-italic">A. ursinum</span>; CP = <span class="html-italic">C. pilosa</span>; MU = <span class="html-italic">M. uniflora</span>.</p> Full article ">Figure 3
<p>Duncan post hoc test for growth rate and soil origin type showing which soil origin had a similar effect on <span class="html-italic">A. ursinum</span> growth rate in response to total solar radiation, y-axis: quantile-normalised values of growth rate (order of magnitude similar to the values of the sum of solar radiation. Abbreviations: AU—<span class="html-italic">A. ursinum</span>; CP—<span class="html-italic">C. pilosa</span>; MU—<span class="html-italic">M. uniflora</span>; CNT—Control; 1/2/3—site name. Different letters (a, b, c, d, e) above the symbols refer to significant differences (<span class="html-italic">p</span> &lt; 0.05) between the effect of soil origins on growth rates.</p> Full article ">Figure 4
<p>Cross categories box-plots of R.I.I. (Relative Interaction Index) values based on shoots (Total Leaf Area) and roots (Numbers of Roots) of <span class="html-italic">A. ursinum</span> and <span class="html-italic">C. majalis</span> in three moisture categories. The values above the zeroth fainted dotted line are positive R.I.I. values signifying facilitation, while below this are negative R.I.I. values indicating competition. Both seasons’ mean (2017–2018) regenerated plants were harvested by the following spring season. ‘x’ indicates the mean R.I.I. values in all the box plots. Abbreviations: AU—<span class="html-italic">A. ursinum</span>; CM—<span class="html-italic">C. majalis</span>; reg.—regenerated.</p> Full article ">Figure 5
<p>Cross categories box-plots of R.I.I. (Relative Interaction Index) values based on shoots (Total Leaf Area) and roots (Numbers of Roots) of <span class="html-italic">A. ursinum</span> and <span class="html-italic">C. pilosa</span> in three sand categories. Values above the zeroth fainted dotted line are positive R.I.I. values signifying facilitation, while below this are negative R.I.I. values indicating competition. ‘Wet state’, denoted with number 1, is water-saturated soils, and ‘both seasons’ means regenerated plants were harvested by the next spring season (2017–2018). ‘x’ indicates the mean R.I.I. values in all the box plots. Abbreviations: AU—<span class="html-italic">A. ursinum</span>; CP—<span class="html-italic">C. pilosa</span>; reg.—regenerated.</p> Full article ">Figure 6
<p>Cross categories box-plots of R.I.I. (Relative Interaction Index) values based on shoots (Total Leaf Area) and roots (Numbers of Roots) of <span class="html-italic">A. ursinum</span> and <span class="html-italic">M. uniflora</span> in three sand categories. Values above the zeroth fainted dotted line are positive R.I.I. values signifying facilitation, while below this are negative R.I.I. values indicating competition. ‘Wet state’, denoted with number 1, is water-saturated soils, and ‘both seasons’ means regenerated plants were harvested by the next spring season (2017–2018). ‘x’ indicates the mean R.I.I. values in all the box plots. Abbreviations: AU—<span class="html-italic">A. ursinum</span>; MU—<span class="html-italic">M. uniflora</span>; reg.—regenerated.</p> Full article ">Figure 7
<p>Cross categories box-plots of R.I.I. (Relative Interaction Index) values based on shoots (Total Leaf Area) and roots (Numbers of Roots) of <span class="html-italic">A. ursinum</span> and <span class="html-italic">C. pilosa</span> in three sand categories. Values above the zeroth fainted dotted line are positive R.I.I. values signifying facilitation, while below this are negative R.I.I. values indicating competition. ‘Moist state’, denoted with number 2, is moderately water-saturated soils. ‘x’ indicates the mean R.I.I. values in all the box plots. Abbreviations: AU—<span class="html-italic">A. ursinum</span>; CP—<span class="html-italic">C. pilosa</span>.</p> Full article ">Figure 8
<p>Cross categories box-plots of R.I.I. (Relative Interaction Index) values based on shoots (Total Leaf Area) and roots (Numbers of Roots) of <span class="html-italic">A. ursinum</span> and <span class="html-italic">M. uniflora</span> in three sand categories. Values above the zeroth fainted dotted line are positive R.I.I. values signifying facilitation, while below this are negative R.I.I. values indicating competition. ‘Moist state’, denoted with number 2, is moderately water-saturated soils. ‘x’ indicates the mean R.I.I. values in all the box plots. Abbreviations: AU—<span class="html-italic">A. ursinum</span>; MU—<span class="html-italic">M. uniflora</span>.</p> Full article ">
28 pages, 1054 KiB  
Article
Analysis of Farm Data License Agreements: Do Data Agreements Adequately Reflect on Farm Data Practices and Farmers’ Data Rights?
by Jasmin Kaur and Rozita Dara
Agriculture 2023, 13(11), 2170; https://doi.org/10.3390/agriculture13112170 - 20 Nov 2023
Cited by 2 | Viewed by 1622
Abstract
Farm data license agreements are legal documents that play an important role in informing farmers about farm data processing practices such as collection, use, safeguarding, and sharing. These legal documents govern the exchange, access, and dissemination of farm data and are expected to [...] Read more.
Farm data license agreements are legal documents that play an important role in informing farmers about farm data processing practices such as collection, use, safeguarding, and sharing. These legal documents govern the exchange, access, and dissemination of farm data and are expected to provide legal protection against misuse of data. Despite their significant influence on farm data processing and governance, there is limited understanding of the content of farm data license agreements and standards for drafting them. Although online privacy policy content has been extensively studied, farm data agreements’ evaluation and analysis have been overlooked. This study aims to investigate the structure, content, and transparency of farm data licenses. We collected 141 agricultural terms of use agreements and used natural language processing methods such as keyword and keyphrase analysis to perform text feature analysis, Flesch Readability Ease Score and Flesch Grade Level readability analysis, transparency analysis, and content analysis to gain insight into common data practices adopted by the agriculture technology providers. We also manually reviewed these agreements to validate the results and strengthen the observations. The findings show that data agreements are long, complex, and difficult to read and comprehend. The results suggest that 95% of the agreements fall under the difficult-to-read category and close to 75% of the policies require university-level education to understand the content. Furthermore, it is noted that some of the data management practices are not given adequate attention and are not as frequently mentioned in the agreements as expected. Finally, our analysis enabled us to provide recommendations on the content of farm data license agreements and strategies to improve them. Full article
(This article belongs to the Section Agricultural Economics, Policies and Rural Management)
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<p>Overview of the framework for data license agreement evaluation.</p> Full article ">Figure 2
<p>Analysis of correlation between Flesch Reading Ease Score and Passive Voice Index.</p> Full article ">Figure 3
<p>Topic coverage in 141 FDLAs—Average of related Keywords Per Agreement.</p> Full article ">
14 pages, 1504 KiB  
Article
The Spanish Olive Oil with Quality Differentiated by a Protected Designation of Origin
by Ana García-Moral, Encarnación Moral-Pajares and Leticia Gallego-Valero
Agriculture 2023, 13(11), 2169; https://doi.org/10.3390/agriculture13112169 - 20 Nov 2023
Cited by 3 | Viewed by 1764
Abstract
The Protected Designation of Origin (PDO), part of the EU’s quality policy for agri-food products, aims to provide consumers with reliable information on the quality of a food, linked to its origin. Olive oil has perceptible qualities derived from its place of production, [...] Read more.
The Protected Designation of Origin (PDO), part of the EU’s quality policy for agri-food products, aims to provide consumers with reliable information on the quality of a food, linked to its origin. Olive oil has perceptible qualities derived from its place of production, which create a link between the product and its place of origin, and which can influence consumer preferences. Spain, the world’s leading producer of this vegetable fat, had 29 PDOs at the end of 2020, 25.84% of the EU total for this industry. Based on the arguments drawn from the literature and the information provided by the Spanish Ministry of Agriculture, Fisheries and Food (MAPA), this paper first analyses the importance of olive oil with differentiated quality certified by a PDO for the Spanish olive oil industry. Secondly, the t-test is applied to identify positive differences in the income earned by farmers who produce olive oil certified by a PDO. Thirdly, the international competitiveness of extra virgin olive oil (EVOO) bearing a PDO label is analysed using the Revealed Comparative Advantage (RCA) index. The evidence confirms that PDO certification adds value to the product and promotes exports. However, the Spanish olive oil industry does not perform well enough to harness the potential offered by this quality label, it as it does not manage to sell all the PDO-certified EVOO. This situation merits further investigation in future studies, and should be taken into account in the design of actions and campaigns organised by institutions involved in the industry. This article contributes to the evaluation of the quality policy for EU agri-food products and examines the recent evolution of the Spanish PDO-certified olive oil industry. Full article
(This article belongs to the Section Agricultural Economics, Policies and Rural Management)
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<p>Distribution of global olive oil production by country in the 2020–2021 season. Source: [<a href="#B6-agriculture-13-02169" class="html-bibr">6</a>].</p> Full article ">Figure 2
<p>PDO-certified EVOO from Spain. Source: [<a href="#B17-agriculture-13-02169" class="html-bibr">17</a>].</p> Full article ">Figure 3
<p>EU Protected Designation of Origin label. Source: [<a href="#B57-agriculture-13-02169" class="html-bibr">57</a>].</p> Full article ">Figure 4
<p>Percentage share of SV EVOO-PDO in SV Agrifood-PDO between 2008 and 2020. Source: [<a href="#B54-agriculture-13-02169" class="html-bibr">54</a>].</p> Full article ">
18 pages, 1142 KiB  
Article
Diet Influence on Sperm Quality, Fertility, and Reproductive Behavior in Karakul of Botoșani Rams
by Constantin Pascal, Ionică Nechifor, Marian Alexandru Florea, Claudia Pânzaru, Daniel Simeanu and Daniel Mierliță
Agriculture 2023, 13(11), 2168; https://doi.org/10.3390/agriculture13112168 - 19 Nov 2023
Cited by 1 | Viewed by 1748
Abstract
This study aims to analyze the influence of an improved diet with vitamins and minerals (VM) on the live weight, body condition, quality of sperm, behavior, and fertility of rams. The biological material comprised two groups of rams (L1—control and L2—VM supplemented), each [...] Read more.
This study aims to analyze the influence of an improved diet with vitamins and minerals (VM) on the live weight, body condition, quality of sperm, behavior, and fertility of rams. The biological material comprised two groups of rams (L1—control and L2—VM supplemented), each consisting of 15 individuals. After a complete one-year cycle, they received different dietary treatments at the beginning of the preparation for the reproduction period. Although in the onset of the mounting period (SM), no significant differences were observed for live weight (p > 0.05), providing supplemental feeding of a VM complex allowed a better capitalization of body reserves, and, consequently, the rams’ groups differed significantly by the end of mating season (FM), for live weight (+4.1%; p < 0.001) and body condition score (+15.9%; p < 0.05). Adding vitamins and minerals to the L2 diet also improved sperm color (p < 0.001), sperm concentration (+11.8%; p < 0.01), live spermatozoa (+2.6%; p < 0.001), and decreased abnormal spermatozoa proportion (−7.0%; p < 0.01). The increase in the scrotum circumference in L2 (+4.57%) suggests that VM supplements improved testosterone secretion, spermatogenesis, and ejaculate volume (+10.20%; (p < 0.001), with a positive impact (p < 0.001) on mating behavior, on the gestation installation (+11.2%) and on the number of obtained lambs (+14.0%), as well as on the key economic indicators (+13.8% incomes per ram). Full article
(This article belongs to the Special Issue Animal Nutrition and Productions: Series II)
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<p>Representation of the research area (<b>a</b>) and its location on the map of Romania (<b>b</b>).</p> Full article ">Figure 2
<p>Semen collection (<b>a</b>) and scrotal circumference measuring (<b>b</b>).</p> Full article ">
16 pages, 4877 KiB  
Article
Improved Method for Apple Fruit Target Detection Based on YOLOv5s
by Huaiwen Wang, Jianguo Feng and Honghuan Yin
Agriculture 2023, 13(11), 2167; https://doi.org/10.3390/agriculture13112167 - 18 Nov 2023
Cited by 6 | Viewed by 2252
Abstract
Images captured using unmanned aerial vehicles (UAVs) often exhibit dense target distribution and indistinct features, which leads to the issues of missed detection and false detection in target detection tasks. To address these problems, an improved method for small target detection called YOLOv5s [...] Read more.
Images captured using unmanned aerial vehicles (UAVs) often exhibit dense target distribution and indistinct features, which leads to the issues of missed detection and false detection in target detection tasks. To address these problems, an improved method for small target detection called YOLOv5s is proposed to enhance the detection accuracy for small targets such as apple fruits. By applying improvements to the RFA module, DFP module, and Soft-NMS algorithm, as well as integrating these three modules together, accurate detection of small targets in images can be achieved. Experimental results demonstrate that the integrated, improved model achieved a significant improvement in detection accuracy, with precision, recall, and mAP increasing by 3.6%, 6.8%, and 6.1%, respectively. Furthermore, the improved method shows a faster convergence speed and lower loss value during the training process, resulting in higher recognition accuracy. The results of this study indicate that the proposed improved method exhibits a good performance in apple fruit detection tasks involving UAV imagery, which is of great significance for fruit yield estimation. The research findings demonstrate the effectiveness and feasibility of the improved method in addressing small target detection tasks, such as apple fruit detection. Full article
(This article belongs to the Special Issue Model-Assisted and Computational Plant Phenotyping)
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<p>Schematic diagram of on-site image collection by drone.</p> Full article ">Figure 2
<p>Original image of apple tree (<b>left</b>) and masked image (<b>right</b>).</p> Full article ">Figure 3
<p>Schematic diagram of dataset supplementation.</p> Full article ">Figure 4
<p>Network model diagram of YOLOv5s.</p> Full article ">Figure 5
<p>Detailed structure of RFA.</p> Full article ">Figure 6
<p>Detailed structures of DFP and PAN.</p> Full article ">Figure 7
<p>Training results for the improved YOLOv5s network.</p> Full article ">Figure 8
<p>Detection results for improved YOLOv5s network.</p> Full article ">
11 pages, 6554 KiB  
Article
Deletion of the OsLA1 Gene Leads to Multi-Tillering and Lazy Phenotypes in Rice
by Zhanglun Sun, Tianrun Mei, Tingting Feng, Hao Ai, Yafeng Ye, Sumei Duan, Binmei Liu and Xianzhong Huang
Agriculture 2023, 13(11), 2166; https://doi.org/10.3390/agriculture13112166 - 17 Nov 2023
Cited by 2 | Viewed by 1526
Abstract
Plant architecture, one of the key factors that determine grain yield in rice, is mainly affected by components such as plant height, tiller number, and panicle morphology. For this paper, we obtained a multi-tillering and lazy mutant from a japonica rice cultivar, Wuyunjing [...] Read more.
Plant architecture, one of the key factors that determine grain yield in rice, is mainly affected by components such as plant height, tiller number, and panicle morphology. For this paper, we obtained a multi-tillering and lazy mutant from a japonica rice cultivar, Wuyunjing 7 (WYJ7), via treatment with a heavy ion beam. Compared to WYJ7, the mutant showed a significant increase in tiller angle, tiller number, number of primary and secondary branches, and number of grains; however, the plant height and grain thickness of the mutant was significantly decreased. Phenotypic analysis of the F1 hybrids revealed that the multi-tillering and lazy mutant phenotypes were regulated by a recessive gene. The segregation ratio of 1׃3 of the mutant phenotype and the wild-type plant in the F2 population indicated that the former was controlled by a single gene named Multi-Tillering and Lazy 1 (MTL1). Bulked segregant analysis was performed using the individual plants with extremely typical tiller angles in the F2 population. The MTL1 gene was initially mapped within a region of 5.58–17.64 Mb on chromosome 11. By using the F2 segregated population for fine mapping, the MTL1 gene was ultimately fine mapped within the range of 66.67 kb on chromosome 11. The analysis of genes in this region revealed the presence of the previously identified LAZY1 (LA1) gene. Genomic PCR amplification and semi-quantitative RT-PCR assays showed that the LA1 gene could not be amplified and was not expressed, thus indicating that the MTL1 gene might be identical to the LA1 gene. This study suggests that the multi-tillering and lazy mutant phenotypes might be caused by the deletion of LA1 function. This finding can guide further investigations on the functional mechanisms of the LA1 gene, thus enriching the theoretical knowledge of plant architecture in relation to rice. Full article
(This article belongs to the Special Issue Genetic Identification, Exploration, Evaluation and Breeding of Plant Genetic Resources)
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<p>Comparison of plant types between WYJ7 and <span class="html-italic">mtl1</span> rice under field growth conditions. (<b>a</b>) Plant phenotypes grown under field conditions; (<b>b</b>) tiller angle; (<b>c</b>,<b>d</b>) tiller number; (<b>e</b>,<b>f</b>) plant height. Scale bar: 15 cm.</p> Full article ">Figure 2
<p>Comparison of panicle patterns between WYJ7 and <span class="html-italic">mtl1</span> rice. (<b>a</b>) Panicle type; (<b>b</b>) grains of an individual plant; (<b>c</b>) panicle length; (<b>d</b>) number of primary branches; (<b>e</b>) number of secondary branches; (<b>f</b>) number of grains per panicle; (<b>g</b>) grain yield per plant. Scale bar: 5 cm.</p> Full article ">Figure 3
<p>Grain type comparison between WYJ7 and <span class="html-italic">mtl1</span> rice. (<b>a</b>,<b>b</b>) Grain length; (<b>c</b>,<b>d</b>) grain width; (<b>e</b>,<b>f</b>) grain thickness; (<b>g</b>) 1000-grain weight. Scale bar: 1 cm.</p> Full article ">Figure 4
<p>Preliminarily mapping of the <span class="html-italic">MTL1</span> gene using the BSA method. (<b>a</b>) Genotype analysis of parents and the mixed pools of F<sub>2</sub>; (<b>b</b>,<b>c</b>) SNP analysis of the differential loci. Homozygous gene loci in the female parent P<sub>1</sub>-<span class="html-italic">mtl1</span> are shown in gray (AA); homozygous loci in the male parent P<sub>2</sub>-HJX74 are shown in red (BB); heterozygous gene loci in the segregated populations are shown in blue (AB).</p> Full article ">Figure 5
<p>Fine mapping and candidate gene analysis. (<b>a</b>) Preliminary mapping of <span class="html-italic">MTL1</span>; (<b>b</b>) fine mapping of <span class="html-italic">MTL1</span> by using newly designed InDels; (<b>c</b>) candidate genes in the mapping interval. (<b>d</b>) PCR amplification of the exons of <span class="html-italic">LA1</span>; (<b>e</b>) semi-quantitative RT-PCR analysis of <span class="html-italic">LA1</span> expression level. Ms represent polymorphic markers; the numbers below the lines represent the number of recombinants. The arrow indicates the site of the predicted genes in the M8 to M12 interval.</p> Full article ">
33 pages, 2477 KiB  
Review
A Review: Development of Plant Protection Methods and Advances in Pesticide Application Technology in Agro-Forestry Production
by Jiaqiang Zheng and Youlin Xu
Agriculture 2023, 13(11), 2165; https://doi.org/10.3390/agriculture13112165 - 17 Nov 2023
Cited by 5 | Viewed by 4782
Abstract
In this review, through reviewing the history of the struggle between human beings and plant diseases, insects and weeds, more specifically thoughts on plant protection in ancient Chinese agricultural books, the recognition of plant pests as a target and six types of plant [...] Read more.
In this review, through reviewing the history of the struggle between human beings and plant diseases, insects and weeds, more specifically thoughts on plant protection in ancient Chinese agricultural books, the recognition of plant pests as a target and six types of plant protection methods and 36 subdivision measures are summarized. Then, we focus on the development overview of pesticide application technology and conduct a systematic review by combining the development timeline of pesticide application and key technologies including performance measurement and the simulation and modeling of pesticide-spraying systems. Finally, three suggestions for further research are proposed from the perspectives of human beings’ and environmental health, sustainable and eco-friendly application media and efficient application equipment systems in plant protection. Full article
(This article belongs to the Special Issue Pesticide Application Technology in Cropping Systems)
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<p>Plant protection methods and pesticide application technologies.</p> Full article ">Figure 2
<p>Plant protection methods and subdivision measures.</p> Full article ">Figure 3
<p>Timeline of major developments in pesticide application technology.</p> Full article ">Figure 4
<p>Comprehensive performance analysis diagram of pesticide-spraying process.</p> Full article ">
20 pages, 1494 KiB  
Article
The Effects of Dissolved Organic Matter Derived from Agricultural Waste Materials on Phosphorus Sorption in Sandy Soils
by Magdalena Debicka, Mohsen Morshedizad and Peter Leinweber
Agriculture 2023, 13(11), 2164; https://doi.org/10.3390/agriculture13112164 - 17 Nov 2023
Cited by 4 | Viewed by 1975
Abstract
The effect of organic matter (OM) on soil phosphorus (P) sorption is controversial, as there is still no clear answer whether organic matter inhibits or increases P sorption. Despite the great need for renewable sources of available P and OM in agricultural soils, [...] Read more.
The effect of organic matter (OM) on soil phosphorus (P) sorption is controversial, as there is still no clear answer whether organic matter inhibits or increases P sorption. Despite the great need for renewable sources of available P and OM in agricultural soils, little is known about the interaction between P and dissolved organic matter (DOM) in natural soil systems. The aim of this research was to uncover if and how soil saturation with DOM derived from different types of abundant agricultural wastes (cattle manure, horse manure, biogas digestate, compost) affects the phosphate sorption. We examined the P sorption process in control and DOM-saturated sandy soils. The results indicated that OM introduced with agricultural waste did not always reduce P sorption, but certainly had an effect on impairing P fixation, and thus, may result in potentially greater P mobility in the soil, including P availability. Among these waste materials, DOM from horse manure had the most positive effect on P mobilization; thus, horse manure—if available—is recommended for spreading on soils with low P mobility. Full article
(This article belongs to the Section Agricultural Soils)
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<p>Phosphorus sorption in soils under study (S1, S2, S3). Explanation: Q—the amount of P adsorbed to soil at equilibrium concentration; C<sub>eq</sub>—the P concentration in the equilibrated solution.</p> Full article ">Figure 2
<p>Phosphorus sorption after DOM saturation of soils S1 (<b>A</b>), S2 (<b>B</b>), and S3 (<b>C</b>). Symbols: S1, S2, S3—control soils; DOM types: CPT—compost, BD—biogas digestate, CM—cattle manure, HM—horse manure; symbols for soil 1: S1-CPT—soil 1 with compost DOM addition; S1-CM—soil 1 with cattle manure DOM addition; S1-HM—soil 1 with horse manure DOM addition; S1-BD—soil 1 with biogas digestate DOM. The same pattern was used for other soils. Q—the amount of P adsorbed to soil at equilibrium concentration; C<sub>eq</sub>—the P concentration in the equilibrated solution.</p> Full article ">Figure 3
<p>Changes of Freundlich isotherm parameters in S1, S2, S3 soils after DOM application: (<b>A</b>). Changes of K<sub>f</sub> parameter; (<b>B</b>). Changes of n<sub>f</sub> parameter. Changes (expressed in %) for the values of each parameter were related to the values of the control samples, which are displayed here as level 0.</p> Full article ">Figure 4
<p>Changes of Langmuir isotherm parameters: (<b>A</b>) Q<sub>m</sub>, (<b>B</b>) K<sub>L</sub>, and (<b>C</b>) MBC in S1, S2, S3 soils after DOM application. Changes (expressed in %) of each parameter values were related to the values of the control samples, which are displayed here as level 0.</p> Full article ">Figure 5
<p>Projection of variables onto the factor plane (1 × 2).</p> Full article ">Figure 6
<p>Projection of cases onto the factor plane (1 × 2).</p> Full article ">
21 pages, 2489 KiB  
Article
Influence of Soil Type, Land Use, and Rootstock Genotype on Root-Associated Arbuscular Mycorrhizal Fungi Communities and Their Impact on Grapevine Growth and Nutrition
by Rosalba O. Fors, Emilia Sorci-Uhmann, Erika S. Santos, Patricia Silva-Flores, Maria Manuela Abreu, Wanda Viegas and Amaia Nogales
Agriculture 2023, 13(11), 2163; https://doi.org/10.3390/agriculture13112163 - 17 Nov 2023
Cited by 6 | Viewed by 2118
Abstract
Soil characteristics, land management practices, and plant genotypes influence arbuscular mycorrhizal fungi (AMF) communities, leading to the proliferation of AMF taxa with different growth and nutritional outcomes in their hosts. However, the specific patterns driving these relationships are still not well understood. This [...] Read more.
Soil characteristics, land management practices, and plant genotypes influence arbuscular mycorrhizal fungi (AMF) communities, leading to the proliferation of AMF taxa with different growth and nutritional outcomes in their hosts. However, the specific patterns driving these relationships are still not well understood. This study aimed to (1) evaluate the influence of soil characteristics, land use, and rootstock on AMF diversity and community structure and (2) assess the effect of those AMF communities on grapevine growth and nutrition. Soil samples were collected from vineyard and non-agricultural areas in Lisbon and Pegões, Portugal, and trap cultures established using Richter 110 and 1103 Paulsen rootstocks. After 3.5 months growth under greenhouse conditions, root-associated AMF communities were assessed by amplicon metagenomic sequencing using AMF-specific primers. Alpha diversity was only influenced by the soil type, while in β-diversity, an interaction was found between the soil type and land use. Both diversity measures were positively correlated with foliar K and negatively with leaf Mn and Mg. Notably, the concentrations of these nutrients were highly correlated with the relative abundance of operational taxonomic units (OTUs) within the genera Glomus, Rhizophagus, and Claroideoglomus. These results are valuable for supporting AMF selection for improved plant nutrition based on varying soil types and land uses. Full article
(This article belongs to the Special Issue Ecological Environment and Microbial Community of Agricultural Soils)
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<p>Alpha-diversity indices, i.e., OTU richness and Shannon index, in roots of Aragonez vine plants grafted onto Richter 110 (R110) or 1103 P (1103 P) rootstocks grown in vineyard or non-agricultural soils from Lisbon and Pegões. Bars indicate average values (<span class="html-italic">n</span> = 3 ± standard error). The effect of the soil type (collected from Lisbon or Pegões locations) was significant for all the three indices.</p> Full article ">Figure 2
<p>Venn diagrams representing shared and exclusive OTUs in grapevine roots according to the (<b>A</b>) soil type (from Lisbon or Pegões), (<b>B</b>) land use, and (<b>C</b>) rootstock.</p> Full article ">Figure 3
<p>(<b>A</b>) Arbuscular mycorrhizal fungal community composition in roots of Aragonez vine plants grafted onto Richter 110 (R110) or 1103 P (1103 P) rootstocks grown in vineyard (VS) or non-agricultural (NAS) soils from Lisbon and Pegões. (<b>B</b>) Frequencies of low-abundant (less than 5%) mycorrhizal fungal genera.</p> Full article ">Figure 4
<p>Principal coordinate analysis conducted with Bray–Curtis distances of root-associated mycorrhizal communities from Lisbon’s and Pegões’ non-agricultural soils (NAS) and vineyard soils (VS).</p> Full article ">Figure 5
<p>Mycorrhizal colonization (%) in Aragonez variety grapevines grafted onto Richter 110 (R110) or 1103 Paulsen (1103 P) rootstocks grown in vineyard or non-agricultural soils from Lisbon and Pegões. Bars indicate average values ± standard error (<span class="html-italic">n</span> = 5). Above each location, the results of the two-way ANOVA for land use and rootstock effects and their interaction are shown. Asterisks indicate significant effect, and “ns” indicates non-significant effect at <span class="html-italic">p</span> = 0.05. Different letters indicate significant differences according to Duncan <span class="html-italic">post hoc</span> test conducted to compare experimental groups within each soil type.</p> Full article ">Figure 6
<p>Correlation analysis (Spearman’s <span class="html-italic">rho</span>) between shoot length, root biomass, mycorrhizal colonization (Myc_col), and leaf nutrient concentration and α-diversity indexes.</p> Full article ">Figure 7
<p>Mantel correlation plot for leaf nutrient concentration distance matrix (based on Euclidean distances) and Bray–Curtis dissimilarity index matrix of root AMF communities. Mantel statistic (<span class="html-italic">r</span>) and associated <span class="html-italic">p</span>-value are shown in the upper-left corner of the plot.</p> Full article ">
30 pages, 8878 KiB  
Article
Development of Boom Posture Adjustment and Control System for Wide Spray Boom
by Jinyang Li, Zhenyu Nie, Yunfei Chen, Deqiang Ge and Meiqing Li
Agriculture 2023, 13(11), 2162; https://doi.org/10.3390/agriculture13112162 - 17 Nov 2023
Cited by 3 | Viewed by 2153
Abstract
To obtain a more consistent droplet distribution and reduce spray drift, it is necessary to keep the entire spray boom parallel to the crop canopy or ground and maintain a certain distance from the spray nozzles to the crop canopy or ground. A [...] Read more.
To obtain a more consistent droplet distribution and reduce spray drift, it is necessary to keep the entire spray boom parallel to the crop canopy or ground and maintain a certain distance from the spray nozzles to the crop canopy or ground. A high-performance boom active control system was developed for boom trapezoid suspension. The hydraulic system and hardware circuit of the boom control system were designed based on analyzing the configuration of active trapezoid suspension. The mathematical models of valve-controlled hydraulic cylinders and active boom suspensions were developed. Step response and frequency domain response analysis of passive suspension were conducted by Simulink simulations, and then key parameters of the boom suspension and hydraulic system were determined. A feedforward proportion integration differentiation (FPID) control algorithm was proposed to improve the tracking performance. The designed control system was assembled on a 24 m boom with trapezoid suspension. The response characteristic of the active boom control system was tested by the step signal and the sinusoidal signal from a six-degree-of-freedom hydraulic motion platform. Firstly, the tracking performance of the active balance control system for the PID (proportion integration differentiation) and FPID control algorithms was compared for a given 0.2 Hz sine signal. Then, for the ground-following control system, the response characteristics in challenging terrain and tracking performance in less challenging terrain were tested. Field experiment results indicate that the maximum rolling angle of the chassis was 3.896° while the maximum inclination angle of the boom was 0.453°. The results show that the designed boom adjustment and control system can effectively adjust the boom motion in real time and meet the requirements of field operation. Full article
(This article belongs to the Special Issue Intelligent Agricultural Machinery and Robots: Embracing Technological Advancements for a Sustainable and Highly Efficient Agricultural Future)
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<p>Boom suspension geometry. (<b>a</b>) boom balance; (<b>b</b>) boom imitation; (<b>c</b>) boom balance and boom imitation: 1, 8, 12—Ultrasonic sensor; 2, 7—balance hydraulic cylinder; 3, 10—inclination angle sensor; 4—link rods of trapezoidal suspension; 5—boom suspension; 6—boom chassis; 9—mechanical limit device; 11—lift hydraulic cylinder; 13—sprayer boom; 14—left imitating hydraulic cylinder; 15—right imitating hydraulic cylinder; 16—center frame of boom.</p> Full article ">Figure 1 Cont.
<p>Boom suspension geometry. (<b>a</b>) boom balance; (<b>b</b>) boom imitation; (<b>c</b>) boom balance and boom imitation: 1, 8, 12—Ultrasonic sensor; 2, 7—balance hydraulic cylinder; 3, 10—inclination angle sensor; 4—link rods of trapezoidal suspension; 5—boom suspension; 6—boom chassis; 9—mechanical limit device; 11—lift hydraulic cylinder; 13—sprayer boom; 14—left imitating hydraulic cylinder; 15—right imitating hydraulic cylinder; 16—center frame of boom.</p> Full article ">Figure 2
<p>Schematic diagram of the hydraulic system. (<b>A</b>) active balance hydraulic circuit; (<b>B</b>) lifting hydraulic circuit of the entire boom suspension; (<b>C</b>) ground imitation circuit, and (<b>D</b>) oil supply circuit: 1—oil filter; 2—constant displacement pump; 3—overflow valve; 4—oil cooler; 5—constant difference overflow valve; 6—pressure relief opening; 7—damper 8, 9—active balance hydraulic cylinder; 10, 18, 23—one-way throttle valve; 11—shuttle valve; 12—two-position two-way solenoid valve; 13, 20, 21, 22—three-position four-way proportional valve; 14—pressure compensator; 15—sensing one-way valve; 16—lifting hydraulic cylinder; 17, 24—energy accumulator; 19—balance valve; 25, 26—imitation hydraulic cylinder.</p> Full article ">Figure 3
<p>Schematic diagram of the controller.</p> Full article ">Figure 4
<p>Test bench of the boom control system. 1—hydraulic power unit; 2—boom and trapezoid suspension; 3—control valves; 4—control computer of hydraulic test bench; 5—six degrees freedom motion planform; 6—boom controller.</p> Full article ">Figure 5
<p>Tractor and boom suspension. 1—ultrasonic sensor; 2—inclination sensor of boom; 3—inclination sensor of chassis; 4—imitation cylinders; 5—active balance cylinder.</p> Full article ">Figure 6
<p>Boom suspension geometry.</p> Full article ">Figure 7
<p>Ground following geometric model.</p> Full article ">Figure 8
<p>Block diagram of the control system.</p> Full article ">Figure 9
<p>Simulation model of passive boom suspension.</p> Full article ">Figure 10
<p>Step response characteristic curve of boom suspension.</p> Full article ">Figure 11
<p>Frequency response characteristics of passive suspension with different AB lengths.</p> Full article ">Figure 12
<p>Dynamic simulation model for ground following of the boom suspension.</p> Full article ">Figure 13
<p>Relationship between cylinder displacement <math display="inline"><semantics> <mrow> <msub> <mi>x</mi> <mi>p</mi> </msub> </mrow> </semantics></math> and β.</p> Full article ">Figure 14
<p>Relationship between cylinder piston displacement and nozzle end height.</p> Full article ">Figure 15
<p>Force variation in an imitating cylinder.</p> Full article ">Figure 16
<p>Comparison of two controllers for an active boom control system. (<b>a</b>) Tracking perfor− mance for two controllers; rin: input signal; yout: PID; yout-upf: FPID; (<b>b</b>) Local amplification of (<b>a</b>); (<b>c</b>) Tracking error for the two controllers.</p> Full article ">Figure 16 Cont.
<p>Comparison of two controllers for an active boom control system. (<b>a</b>) Tracking perfor− mance for two controllers; rin: input signal; yout: PID; yout-upf: FPID; (<b>b</b>) Local amplification of (<b>a</b>); (<b>c</b>) Tracking error for the two controllers.</p> Full article ">Figure 17
<p>Tracking performance for the ground following the control system. (<b>a</b>) Desired height and measured height; (<b>b</b>) imitating error.</p> Full article ">Figure 18
<p>Height difference between left and right boom arms.</p> Full article ">Figure 19
<p>Rolling angle variation in boom and chassis in a field experiment.</p> Full article ">
15 pages, 1227 KiB  
Article
The Impact of Genotype on Chemical Composition, Feeding Value and In Vitro Rumen Degradability of Fresh and Ensiled Forage of Native Maize (Zea mays L.) from Mexico
by Edwin Rafael Alvarado-Ramírez, Gilberto Ballesteros-Rodea, Abdelfattah Zeidan Mohamed Salem, José Reyes-Hernández, Camelia Alejandra Herrera-Corredor, Javier Hernández-Meléndez, Andrés Gilberto Limas-Martínez, Daniel López-Aguirre and Marco Antonio Rivas-Jacobo
Agriculture 2023, 13(11), 2161; https://doi.org/10.3390/agriculture13112161 - 17 Nov 2023
Cited by 1 | Viewed by 1705
Abstract
The objective of this study was to evaluate the impact of the genotype on the chemical composition, feeding value and in vitro rumen degradability of fresh and ensiled forage of four native maize varieties (Amarillo, Olotillo, Tampiqueño and Tuxpeño) from Tamaulipas, Mexico, and [...] Read more.
The objective of this study was to evaluate the impact of the genotype on the chemical composition, feeding value and in vitro rumen degradability of fresh and ensiled forage of four native maize varieties (Amarillo, Olotillo, Tampiqueño and Tuxpeño) from Tamaulipas, Mexico, and a commercial hybrid, as well as the stability and aerobic deterioration of the silage. In all genotypes, fresh forage consisted of whole plants of maize that were harvested when the grain reached a milky-mass state, and silage was fresh forage chopped and ensiled in plastic bags, where it fermented for 120 days. The hybrid presented the highest content (p < 0.05) of dry matter (DM), organic matter (OM), ether extract, non-fibrous carbohydrates (NFCs) and starch, as well as the lowest content (p < 0.05) of fibers (NDF and ADF), acid detergent lignin and water-soluble carbohydrates (WSCs). Furthermore, the hybrid and Amarillo genotypes obtained the lowest pH and ammoniacal nitrogen content (p < 0.05), intermediate values (p < 0.05) of lactic and butyric acid, and the lowest and highest acetic acid content (p < 0.05), respectively. Although OM did not differ (p > 0.05) between states of the forage, the fresh forage presented a higher (p < 0.05) content of DM, crude protein, NDF, ADF, WSCs, pH and butyric acid in all genotypes, while the rest of the parameters were higher (p < 0.05) in the silage. However, Amarillo obtained the highest feeding value (p < 0.05) in terms of DM intake, relative forage value, digestible energy, metabolizable energy and rumen degradability (DM, NDF and ADF), and between states of the forage, ensiled obtained the highest feeding value (p < 0.05). During the aerobic exposure, the Amarillo and hybrid silage showed greater (p < 0.05) stability (>38 h), and less (p < 0.05) deterioration, pH increase and loss of DM and OM, while Tuxpeño obtained less stability and greater deterioration. In conclusion, the genotype did influence the chemical composition of fresh and ensiled forage, which affected the feeding value and in vitro rumen degradability, and the Amarillo and hybrid genotypes presented the best values in the evaluated parameters. Full article
(This article belongs to the Special Issue Livestock Nutrition: Pasture System and Forage Conservation)
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<p>In vitro rumen degradability of dry matter (DMD); (<b>a</b>), neutral detergent fiber (NDFD); (<b>b</b>) and acid detergent fiber (ADFD); (<b>c</b>) of the fresh and ensiled forage of four genotypes of native maize (<span class="html-italic">Zea mays</span> L.) from Mexico and a commercial hybrid, at 6, 12, 24 and 48 h of incubation. <sup>1</sup> G: genotypes of maize; S: states of the forage; T: incubation time; G × S: interaction between genotypes of maize and states of forage; G × T: interaction between genotypes of maize and incubation time; S × T: interaction between states of the forage and incubation time; G × S × T: interaction genotypes of maize, states of the forage and incubation time; SEM: standard error of the mean.</p> Full article ">Figure 2
<p>pH (<b>a</b>) and dry matter (DM); (<b>b</b>) and organic matter (OM); (<b>c</b>) content of the ensiled forage of four genotypes of native maize (<span class="html-italic">Zea mays</span> L.) from Mexico and a commercial hybrid, at different times of aerobic exposure. <sup>1</sup> G: genotypes of maize; T: aerobic exposure time; G × T: interaction between genotypes of maize and aerobic exposure time; SEM: standard error of the mean.</p> Full article ">Figure 2 Cont.
<p>pH (<b>a</b>) and dry matter (DM); (<b>b</b>) and organic matter (OM); (<b>c</b>) content of the ensiled forage of four genotypes of native maize (<span class="html-italic">Zea mays</span> L.) from Mexico and a commercial hybrid, at different times of aerobic exposure. <sup>1</sup> G: genotypes of maize; T: aerobic exposure time; G × T: interaction between genotypes of maize and aerobic exposure time; SEM: standard error of the mean.</p> Full article ">
22 pages, 13287 KiB  
Article
Path Tracking Control of a Tractor on a Sloping Road with Steering Compensation
by Jieyong Ou, Qiang Fu, Rui Tang, Jianwei Du and Lihong Xu
Agriculture 2023, 13(11), 2160; https://doi.org/10.3390/agriculture13112160 - 16 Nov 2023
Viewed by 1195
Abstract
Agricultural tractors are subject to lateral forces when traveling on slopes, making it difficult to accurately follow a set course. In this paper, a steering compensation method is first proposed based on the force analysis of a tractor traveling on slopes, which compensates [...] Read more.
Agricultural tractors are subject to lateral forces when traveling on slopes, making it difficult to accurately follow a set course. In this paper, a steering compensation method is first proposed based on the force analysis of a tractor traveling on slopes, which compensates the steering angle according to the friction force and gravity force imposed on the tractor. Further, when traveling on slopes, acceleration and a load applied to the tractor are usually time-varying. To address this problem, this paper proposes a steering compensator that can automatically adjust a compensation coefficient, as well as a design for a model predictive controller with the steering compensator for a tractor. Simulation results show that under different traveling speeds, turning radius, and slope angles, the steering compensator allows the tractor to travel more smoothly, i.e., the distance between the actual traveling route and the reference route fluctuates within a smaller range. Further, during straight-line traveling, the static error can be effectively reduced, i.e., the distance between the actual traveling route and the reference route is closer to zero. Overall, the steering compensator enables the tractor to track the reference route more accurately. Full article
(This article belongs to the Section Agricultural Technology)
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<p>Schematic diagram of forces on a tractor traveling on a slope.</p> Full article ">Figure 2
<p>Mechanical relations of a tire during steering.</p> Full article ">Figure 3
<p>Schematic diagram of the steering angle compensation.</p> Full article ">Figure 4
<p>Schematic of the steering angle compensation for path tracking control of the tractor.</p> Full article ">Figure 5
<p>Schematic representation of the topographic conditions of the slope and way of traveling in the simulation.</p> Full article ">Figure 6
<p>(Non) compensated route vs. reference route.</p> Full article ">Figure 7
<p>(Non) compensated route vs. reference route localized (3 km/h).</p> Full article ">Figure 8
<p>(Non) compensated route vs. reference route localized (5 km/h).</p> Full article ">Figure 9
<p>(Non) compensated route vs. reference route localized (7 km/h).</p> Full article ">Figure 10
<p>(Non) compensated route vs. reference route localized (10 km/h).</p> Full article ">Figure 11
<p>(Non) compensated route vs. reference route localized (10 m).</p> Full article ">Figure 12
<p>(Non) compensated route vs. reference route localized (7 m).</p> Full article ">Figure 13
<p>(Non) compensated route vs. reference route localized (5 m).</p> Full article ">Figure 14
<p>(Non) compensated route vs. reference route localized (6.28°).</p> Full article ">Figure 15
<p>(Non) compensated route vs. reference route localized (12.42°).</p> Full article ">Figure 16
<p>(Non) compensated route vs. reference route localized (18.27°).</p> Full article ">Figure 17
<p>Actual vs. reference speed.</p> Full article ">Figure 18
<p>Actual steering angle vs. desired steering angle.</p> Full article ">
14 pages, 4667 KiB  
Article
Dietary Resistant Starch Regulates Bile Acid Metabolism by Modulating the FXR/LRH-1 Signaling Pathway in Broilers
by Zhenxin Wang, Chunyan Zhan, Yingying Zhang, Lin Zhang, Jiaolong Li, Tong Xing, Liang Zhao, Jianfei Wang and Feng Gao
Agriculture 2023, 13(11), 2159; https://doi.org/10.3390/agriculture13112159 - 16 Nov 2023
Cited by 1 | Viewed by 1554
Abstract
This study aimed to investigate the effects of dietary corn-resistant starch on the bile acid metabolism of broilers. In total, 80, 1-day-old male broilers were randomly distributed into two groups fed either the basic normal corn–soybean diet or a diet supplemented with 40 [...] Read more.
This study aimed to investigate the effects of dietary corn-resistant starch on the bile acid metabolism of broilers. In total, 80, 1-day-old male broilers were randomly distributed into two groups fed either the basic normal corn–soybean diet or a diet supplemented with 40 g/kg of corn-resistant starch. The results showed that dietary supplementation of 4% corn-resistant starch increased the F/G during the periods from 21 to 42 d. Resistant starch supplementation reduced the lipid levels in plasma, and the contents of total bile acids were increased with the altered bile acid profile in the ileum. A diet with corn resistant starch decreased the enzyme contents of the classical pathway of bile acid synthesis and activated the signaling pathway of FXR/LRH-1 in the liver. A decreased abundance of Clostridium cluster XIVa was found in the ileal digesta of the resistant starch group, and its abundance was negatively correlated with the level of lithocholic acid. In summary, the RS was effective at reducing broiler plasma and liver lipid levels, which was probably due to the change in bile acid synthesis and reabsorption capacities. These findings provided a unique landscape of the relationship between bile acid metabolism and resistant starch in broilers. Full article
(This article belongs to the Special Issue Nutritional Strategies on Poultry Product Quality)
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<p>Effects of corn RS supplementation on the plasma and liver lipid levels of broilers on d 42. (<b>A</b>) Plasma lipid levels. (<b>B</b>) Liver lipid levels. NC, a basic normal corn–soybean diet; RS, 4% corn resistant starch. All data are represented by the mean ± standard error (n = 5). * Represents significant differences (<span class="html-italic">p</span> &lt; 0.05).</p> Full article ">Figure 2
<p>Effects of corn RS supplementation on the contents of total bile acids in the liver and ileum. (<b>A</b>) Contents of total bile acids in the liver, and (<b>B</b>) contents of total bile acids in the ileum. NC, a basic normal corn–soybean diet; RS, 4% corn resistant starch. All data are represented by the mean ± standard error (n = 5). * Represents significant differences (<span class="html-italic">p</span> &lt; 0.05).</p> Full article ">Figure 3
<p>Effects of corn RS supplementation on the targeted metabolomic of ileum bile acids. (<b>A</b>) Heat map of hierarchical cluster analysis of bile acids. (<b>B</b>) The various types of bile acids. PBA = primary bile acids; SBA = secondary bile acids; FBA = free bile acids; CBA = conjugated bile acids. (<b>C</b>) The relative abundance of the top bile acids. (<b>D</b>) The bile acids with a content greater than 1 nmol/g. NC, a basic normal corn–soybean diet; RS, 4% corn resistant starch. All data are represented by the mean ± standard error (n = 5). * Represents significant differences (<span class="html-italic">p</span> &lt; 0.05).</p> Full article ">Figure 4
<p>Effects of corn RS supplementation on the bile acid synthase in the liver of broilers. NC, a basic normal corn–soybean diet; RS, 4% corn resistant starch. All data are represented by the mean ± standard error (n = 5). * Represents significant differences (<span class="html-italic">p</span> &lt; 0.05).</p> Full article ">Figure 5
<p>Effects of corn RS supplementation on the gene expression of bile acid transporter in the liver and ileum. (<b>A</b>) Relative mRNA expression of liver bile acid transporter. (<b>B</b>) Relative mRNA expression of ileum bile acid transporter. NC, a basic normal corn–soybean diet; RS, 4% corn resistant starch. All data are represented the mean ± standard error (n = 5). * Represents significant differences (<span class="html-italic">p</span> &lt; 0.05).</p> Full article ">Figure 6
<p>Effects of corn RS supplementation on the FXR/LRH-1 and FXR-FGFR4 signaling pathway. (<b>A</b>) The mRNA expression level of FXR/LRH-1 signaling pathway-related genes in the liver. (<b>B</b>) The mRNA expression level of FXR-FGFR4 signaling pathway-related genes in the ileum. NC, a basic normal corn–soybean diet; RS, 4% corn resistant starch. All data are represented by the mean ± standard error (n = 5). * Represents significant differences (<span class="html-italic">p</span> &lt; 0.05).</p> Full article ">Figure 7
<p>Effects of corn RS supplementation on the number of active bacteria in the ileum. (<b>A</b>) The number of bile salt hydrolase (BSH) active bacteria in the ileum. (<b>B</b>) The number of 7α dehydroxylase active bacteria in the ileum. (<b>C</b>) The correlation between bile acid levels and specific active bacteria. NC, a basic normal corn–soybean diet; RS, 4% corn resistant starch. All data are represented by the mean ± standard error (n = 5). * Represents significant differences (<span class="html-italic">p</span> &lt; 0.05).</p> Full article ">
20 pages, 2506 KiB  
Article
Wastewater-Based Nutrient Supply for Lettuce Production in the Infulene Valley, Maputo, Mozambique
by Celma Almerinda Niquice-Janeiro, Andre Marques Arsénio and Jules Bernardus van Lier
Agriculture 2023, 13(11), 2158; https://doi.org/10.3390/agriculture13112158 - 16 Nov 2023
Viewed by 1208
Abstract
This research investigated the contribution of wastewater-based nutrient supply, viz., nitrogen (N), phosphorous (P), and potassium (K), for lettuce production in the Infulene Valley, Mozambique, from July to September 2019. The research was conducted in groundwater- and wastewater-irrigated agricultural plots. Water samples were [...] Read more.
This research investigated the contribution of wastewater-based nutrient supply, viz., nitrogen (N), phosphorous (P), and potassium (K), for lettuce production in the Infulene Valley, Mozambique, from July to September 2019. The research was conducted in groundwater- and wastewater-irrigated agricultural plots. Water samples were collected weekly, soil samples were collected before planting and after harvest, and lettuce samples were collected at harvest time. The nutrient content (N, P, and K) was measured, and a mass balance method was applied. Wastewater had distinctly higher nutrient contents than groundwater, which guaranteed crop nutrition during the growing stage. Wastewater contributed 88%, 96%, and 97% to the N, P, and K requirements, respectively. The crop yield in the wastewater-irrigated areas was 43,8 ± 16 tons/ha, which was higher than 35 ± 8 tons/ha observed for the groundwater-irrigated areas, but results showed no statistically significant differences. Conclusively, wastewater led to reduced soil-nutrient gap and can be a source of nutrients. Therefore, wastewater is regarded as an alternative nutrient source of interest, and if properly applied, it might reduce environmental health hazards, resulting from run-off or leaching of excess nutrients. Full article
(This article belongs to the Special Issue Effects of Wastewater and Pesticides on Soil Fertility and Microbiological Activity)
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<p>The experimental location for groundwater- and wastewater-irrigated areas in the Infulene Valley.</p> Full article ">Figure 2
<p>The electrical conductivity (EC) and pH value in groundwater (GW) and wastewater (WW) during the experimental period.</p> Full article ">Figure 3
<p>The electrical conductivity (dS/m) at different soil depths irrigated with groundwater (SIG) and wastewater (SIW) before planting (1) and after harvest (2).</p> Full article ">Figure 4
<p>pH values at different soil depths irrigated with groundwater (SIG) and wastewater (SIW) before planting (1) and after harvest (2).</p> Full article ">Figure 5
<p>The <math display="inline"><semantics> <mrow> <msubsup> <mrow> <mi mathvariant="normal">N</mi> <mi mathvariant="normal">O</mi> </mrow> <mrow> <mn>3</mn> </mrow> <mrow> <mo>−</mo> </mrow> </msubsup> </mrow> </semantics></math> concentration (mg/kg) in soils irrigated with wastewater (SIW) and groundwater (SIG) before planting (1) and after harvest (2).</p> Full article ">Figure 6
<p>The <math display="inline"><semantics> <mrow> <msubsup> <mrow> <mi mathvariant="normal">N</mi> <mi mathvariant="normal">H</mi> </mrow> <mrow> <mn>4</mn> </mrow> <mrow> <mo>+</mo> </mrow> </msubsup> </mrow> </semantics></math> concentration (mg/kg) in soils irrigated with wastewater (SIW)- and groundwater (SIG)-irrigated soil before planting (1) and after harvest (2).</p> Full article ">Figure 7
<p>The total N concentration (g/kg) in soil irrigated with wastewater (SIW) and groundwater (SIG) before planting (1) and after harvest (2).</p> Full article ">Figure 8
<p>Available <math display="inline"><semantics> <mrow> <msubsup> <mrow> <mi mathvariant="normal">P</mi> <mi mathvariant="normal">O</mi> </mrow> <mrow> <mn>4</mn> </mrow> <mrow> <mn>3</mn> <mo>−</mo> </mrow> </msubsup> </mrow> </semantics></math> concentration (mg/kg) in soil irrigated with wastewater (SIW) and groundwater (SIG), soil before planting (1) and after harvest (2).</p> Full article ">Figure 9
<p>The total P (mg/kg) in soil irrigated with wastewater (SIW) and groundwater (SIG), before planting (1) and after harvest (2).</p> Full article ">Figure 10
<p>Available K concentration (mg/kg) in soil irrigated with wastewater (SIW) and groundwater (SIG) before planting (1) and after harvest (2).</p> Full article ">Figure 11
<p>The total K concentration (mg/kg) in soil irrigated with wastewater (SIW) and groundwater (SIG) before planting (1) and after harvest (2).</p> Full article ">
23 pages, 11245 KiB  
Article
Design and Testing of an Elastic Comb Reciprocating a Soybean Plant-to-Plant Seedling Avoidance and Weeding Device
by Shenghao Ye, Xinyu Xue, Shuning Si, Yang Xu, Feixiang Le, Longfei Cui and Yongkui Jin
Agriculture 2023, 13(11), 2157; https://doi.org/10.3390/agriculture13112157 - 16 Nov 2023
Cited by 4 | Viewed by 1273
Abstract
Although there are existing interplant weed control devices for soybeans, they mostly rely on image recognition and intelligent navigation platforms. Simultaneously, automated weed control devices are not yet fully mature, resulting in issues such as high seedling injury rates and low weeding rates. [...] Read more.
Although there are existing interplant weed control devices for soybeans, they mostly rely on image recognition and intelligent navigation platforms. Simultaneously, automated weed control devices are not yet fully mature, resulting in issues such as high seedling injury rates and low weeding rates. This paper proposed a reciprocating interplant weed control device for soybeans based on the idea of intermittent reciprocating opening and closing of weeding execution components. The device consists of a laser ranging sensor, servo motor, Programmable Logic Controller (PLC), and weeding mechanism. Firstly, this paper explained the overall structure and working principle of the weed control device, and discussed the theoretical analysis and structural design of the critical component, elastic comb teeth. This paper also analyzed the working principle of the elastic comb teeth movement trajectory and seedling avoidance action according to soybean agronomic planting requirements. Then, field experiments were conducted, and the experiment was designed by the quadratic regression general rotation combination experimental method. The number of combs, the speed of the field management robot, and the stabbing depth were taken as the test factors to investigate their effects on the test indexes of weeding rate and seedling injury rate. The experiment utilized a response surface analysis method and designed a three-factor, three-level quadratic regression general rotation combination experimental method. The results demonstrate that the number of comb teeth has the most significant impact on the weeding rate, while the forward speed has the most significant impact on the seedling injury rate. The optimal combination of 29.06 mm stabbing depth, five comb teeth, and a forward speed of 0.31 m/s achieves an optimal operational weeding rate of 98.2% and a seedling injury rate of 1.69%. Under the optimal parameter combination conditions, the machine’s performance can meet the requirements of intra-row weeding operations in soybean fields, and the research results can provide a reference for the design and optimization of mechanical weed control devices for soybean fields. Full article
(This article belongs to the Section Agricultural Technology)
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<p>Weeding device.</p> Full article ">Figure 2
<p>Reciprocating elastic comb weeding mechanism: (<b>a</b>) Structural conceptual diagram: 1. servo motor; 2. frame; 3. guide rail slider; 4. spindle; 5. flange disk; 6. connecting rod; 7. fixing rod; 8. combing plate; 9. elastic comb teeth; (<b>b</b>) actual picture.</p> Full article ">Figure 3
<p>Weed control actuator and sensor position hanging diagram.</p> Full article ">Figure 4
<p>Sketch of the work of the intra-plant weeding mechanism.</p> Full article ">Figure 5
<p>Sensor and weeding control actuator positioning diagram.</p> Full article ">Figure 6
<p>Distance diagram of different measured objects’ diameters.</p> Full article ">Figure 7
<p>Diameter collecting of soybean plants and weeds schematic diagram.</p> Full article ">Figure 8
<p>Height collecting of soybean plants and weeds schematic diagram.</p> Full article ">Figure 9
<p>Distance collecting of soybean plants schematic diagram.</p> Full article ">Figure 10
<p>Soybean leaf disturbance schematic diagram.</p> Full article ">Figure 11
<p>Imitation elastic comb teeth.</p> Full article ">Figure 12
<p>Schematic diagram of the movement trajectory of the weeding mechanism.</p> Full article ">Figure 13
<p>Weeding mechanism movement trajectory analysis diagram.</p> Full article ">Figure 14
<p>Laser ranging sensor installation location schematic.</p> Full article ">Figure 15
<p>Control box physical diagram.</p> Full article ">Figure 16
<p>Flowchart of one cycle of seedling avoidance action.</p> Full article ">Figure 17
<p>Weed control device installation position.</p> Full article ">Figure 18
<p>Test scenario.</p> Full article ">Figure 19
<p>Weed control effect of soybean intra-plant weeding device: (<b>a</b>) before weeding; (<b>b</b>) after weeding.</p> Full article ">Figure 20
<p>Response surface analysis: (<b>a</b>) The response surfaces of the weeding rate at different numbers of comb teeth and stabbing depth. (<b>b</b>) The response surfaces of seedling injury rate at different forward speeds and stabbing depth.</p> Full article ">
14 pages, 701 KiB  
Article
Influence of Guar Meal from Pig Compound Feed on Productive Performance, Nitrogen Metabolism, and Greenhouse Gas Emissions
by Gabriel Mihaila, Mihaela Habeanu, Nicoleta Lefter, Anca Gheorghe, Mihaela Dumitru, Iuliana Marin, Livia Vidu, Carmen Georgeta Nicolae, Dana Popa and Monica Marin
Agriculture 2023, 13(11), 2156; https://doi.org/10.3390/agriculture13112156 - 16 Nov 2023
Cited by 1 | Viewed by 1465
Abstract
Guar (Cyamopsis tetragonoloba) is an annual legume tolerant to drought. Guar meal (GM) is a protein- and carbohydrate-rich co-product generated after the mechanical separation of the endosperm from the germ and hull of guar seed. GM has received considerable interest in [...] Read more.
Guar (Cyamopsis tetragonoloba) is an annual legume tolerant to drought. Guar meal (GM) is a protein- and carbohydrate-rich co-product generated after the mechanical separation of the endosperm from the germ and hull of guar seed. GM has received considerable interest in animal feed as an alternative to soybean meal (SM). In this study, we aimed to assess the nitrogen (N) balance indicators, performance, carcass traits, and main greenhouse gas (GHG) emissions resulting from enteric fermentation (E-CH4) and manure (M-CH4 and N2O). Two tests were performed: (i) a biological trial on 45 pigs (15 animals/group) and (ii) a digestibility test in metabolism cages (N = 15, 5 replicates/group). Three different diets were given to the pigs: one diet was based on 0% GM (SM diet); in the second, GM-50%, GM replaced 50% of the SM; and the third was GM-100%, in which GM fully replaced the SM. The GM and SM diets were analyzed for their proximate composition. A model based on prediction equations was used to estimate the GHGs. GM up to 10% in the diets of finishing pigs did not significantly impact growth performance or carcass traits, although a slight increase in neutral detergent fiber (NDF) was observed. GM up to 10% improved N digestibility (p < 0.0001), net protein utilization (p < 0.0001), the biological value of protein, coefficients of metabolizability, and the coefficient of the total tract’s apparent digestibility. Irrespective of its dietary proportion, GM decreased total nitrogen output (TNO, p = 0.11). A highly significant impact was noted for N2O and E-CH4 (for DM, p < 0.0001), as well as a significant impact for E-CH4, expressed as g CO2 Eq (p = 0.007), and g CO2 Eq. LU (livestock unit, p = 0.005), also reported as ADG (p = 0.024). Manure, M-CH4, was not significantly influenced. In conclusion, GM can replace up to 100% SM and is thus a valuable byproduct that does not alter animal performance and can positively impact N2O and E-CH4. Full article
(This article belongs to the Special Issue Animal Nutrition and Productions: Series II)
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<p>Intake and descriptive statistics of the growth parameters and carcass traits of the fattening pigs fed two levels of a GM diet that replaced 50% or 100% SM (SM diet). <span class="html-italic">p</span> &gt; 0.05, with no significant difference between the mean. The measurements were performed with PIGLOG 105 on live animals to determine their carcass traits. The number of observations was 48. Abbreviations: average daily feed intake (ADFI, Kg); dry matter intake (DMI, Kg); body weight (BW, Kg); metabolic BW (MBW<sup>0.75</sup>); average daily gain (ADG, Kg); relative growth rate (RGR, %); Kleiber ratio (KR, Kg); LD, <span class="html-italic">Longissimus dorsi</span>; fat thickness (mm); LD area (mm); lean meat (%); standard error of the mean (SEM).</p> Full article ">
11 pages, 1487 KiB  
Article
Effect of Tytanit on Selected Physiological Characteristics, Chemical Composition and Production of Festulolium braunii (K. Richt.) A. Camus
by Jacek Sosnowski and Milena Truba
Agriculture 2023, 13(11), 2155; https://doi.org/10.3390/agriculture13112155 - 15 Nov 2023
Cited by 2 | Viewed by 1169
Abstract
In intensive and sustainable agriculture, it is not enough to use plant protection products and fertilizers, but it is also important to control plant physiological processes. The aim of this study was to determine the effect of Tytanit on Festulolium braunii (K. Richt.) [...] Read more.
In intensive and sustainable agriculture, it is not enough to use plant protection products and fertilizers, but it is also important to control plant physiological processes. The aim of this study was to determine the effect of Tytanit on Festulolium braunii (K. Richt.) A. Camus dry matter yield, photosynthetic activity and the content of chlorophyll and selected chemical compounds. The pot experiment was conducted in 2019 in a plant breeding room. Four levels of treatment were used: control with no treatment and three stimulant concentrations of 0.02%, 0.04% and 0.06% in the spraying liquid. In particular, the research included the determination of dry weight of plant roots, dry weight of plants, chlorophyll a and b content in leaf blades, maximum and actual efficiency of the leaf photosystem, coefficients of non-photochemical and photochemical fluorescence quenching, and the content of total protein, crude fiber, monosaccharides, crude fat, crude ash, Ca, Mg, P and K in the dry matter of plants. Used in controlled conditions, the stimulant contributed to an increase in most parameter values, increasing photosynthetic activity and the content of chlorophyll a and b, total protein, calcium, magnesium and potassium, but it reduced the amounts of crude fiber. Full article
(This article belongs to the Special Issue The Role of Plant Hormones and Growth Regulators in Modern Crop Cultivation Technologies)
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<p>Effect of Tytanit on the amount of <span class="html-italic">Festulolium braunii</span> dry matter (g pot<sup>−1</sup>). Means marked with the same small letters do not differ significantly.</p> Full article ">Figure 2
<p>Effect of Tytanit on the amount of <span class="html-italic">Festulolium braunii</span> dry matter of roots (g pot<sup>−1</sup>). Means marked with the same small letters do not differ significantly.</p> Full article ">Figure 3
<p>The effect of Tytanit on chlorophyll a content (mg 100 g<sup>−1</sup> of fresh weight) in <span class="html-italic">Festulolium braunii</span> (K. Richt.) A. Camus leaves. Means marked with the same small letters do not differ significantly.</p> Full article ">Figure 4
<p>The effect of Tytanit on chlorophyll b content (mg 100 g<sup>−1</sup> of fresh weight) in <span class="html-italic">Festulolium braunii</span> (K. Richt.) A. Camus leaves. Means marked with the same small letters do not differ significantly.</p> Full article ">
17 pages, 2093 KiB  
Article
Agri-Food Supply and Retail Food Prices during the Russia–Ukraine Conflict’s Early Stage: Implications for Food Security
by Mariusz Hamulczuk, Karolina Pawlak, Joanna Stefańczyk and Jarosław Gołębiewski
Agriculture 2023, 13(11), 2154; https://doi.org/10.3390/agriculture13112154 - 15 Nov 2023
Cited by 5 | Viewed by 3024
Abstract
The Russian–Ukrainian conflict has led to the disruption of global supply chains, thus posing a threat to food security. The study aimed to assess the short-term impact of the conflict on food supply and global retail food prices resulting from the disruption of [...] Read more.
The Russian–Ukrainian conflict has led to the disruption of global supply chains, thus posing a threat to food security. The study aimed to assess the short-term impact of the conflict on food supply and global retail food prices resulting from the disruption of agri-food exports from Ukraine after the war outbreak. To assess the impact of the conflict on retail prices worldwide, the actual food price level during the conflict period was compared with the counterfactual values obtained from the forecasting models. The research points to a significant decline in Ukraine’s commodity exports at the beginning of the conflict leading to a supply gap for cereals in particular, affecting global access to staple foods. As a result, global food commodity prices rose sharply, however, the upsurge was short-lived, and as early as July 2022 price indices returned to their pre-war levels. On the other hand, in most regions worldwide the gradual and persistent increase in retail food prices was observed after the war outbreak. The study also found strong regional differentiation in the response of retail food prices to the conflict due to various specific factors that exacerbated or mitigated the impact of the war. Full article
(This article belongs to the Special Issue Sustainable Agriculture and Food Supply: Scientific, Economic and Policy Aspects)
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<p>Wheat exports from Ukraine in 2020–2022. Source: authors’ elaboration from UN Comtrade [<a href="#B34-agriculture-13-02154" class="html-bibr">34</a>].</p> Full article ">Figure 2
<p>Food price indexes according to FAO (2014–2016 = 100). Source: authors’ elaboration from FAOSTAT [<a href="#B35-agriculture-13-02154" class="html-bibr">35</a>].</p> Full article ">Figure 3
<p>Examples of food price forecasts vs. actual data (indexes, 2015 = 100): (<b>a</b>) for Eastern Europe; (<b>b</b>) for Northern Africa. Source: authors’ elaboration from FAOSTAT [<a href="#B35-agriculture-13-02154" class="html-bibr">35</a>].</p> Full article ">Figure 4
<p>Short-run impact channels of the war in Ukraine for food security. Source: authors’ own elaboration.</p> Full article ">
14 pages, 1705 KiB  
Article
The Influence of Ozonation Carried Out during Vegetation on the Content of Selected Bioactive Phytochemicals and the Microbiological Load of Tubers of Raphanus sativus var. sativus
by Miłosz Zardzewiały, Natalia Matłok, Tomasz Piechowiak and Maciej Balawejder
Agriculture 2023, 13(11), 2153; https://doi.org/10.3390/agriculture13112153 - 15 Nov 2023
Viewed by 1162
Abstract
The aim of the study was to determine the effect of ozone gas fumigation on the mechanical, chemical, and microbiological parameters of radish tubers. Radish plants were grown in the ground in accordance with the principles of good agricultural practice and condition suitable [...] Read more.
The aim of the study was to determine the effect of ozone gas fumigation on the mechanical, chemical, and microbiological parameters of radish tubers. Radish plants were grown in the ground in accordance with the principles of good agricultural practice and condition suitable for the soil and climatic conditions of south-eastern Poland. At the end of the growing season, 24 h before harvest, radish plants were exposed to a variable factor, i.e., fumigation with ozone gas at various doses (1 ppm for 1 and 5 min; 5 ppm for 1 and 5 min) in order to modify selected metabolic pathways of bioactive compounds. Then, 24 h after ozonation, radish tubers were harvested and placed in a climatic chamber with controlled conditions, i.e., 2 °C and 90% humidity. Laboratory analyses were performed during storage on days 1, 5, and 10. The ozonation used did not significantly improve the mechanical properties and water content of radish tubers. There was a beneficial effect of selected gaseous ozone doses (1 ppm for 1 and 5 min; 5 ppm for 1 min on the 10th day of storage) on the biosynthesis of selected bioactive compounds, i.e., ascorbic acid content, total polyphenol content, and antioxidant potential during storage. The most beneficial effects of the use of gaseous ozone were observed in the storage process in reducing the microbiological load of radish tubers. Among the ozonation doses used, the dose of 5 ppm for 5 min had the most beneficial effect on reducing the microbiological load. It reduced the number of yeasts and molds by 14.2% and aerobic mesophilic bacteria by 20.9% compared to the control sample on the last day of storage. Additionally, between the 5th and 10th day of storage, a significant effect of each ozone dose applied on reducing the occurrence of yeasts, molds, and mesophilic aerobic bacteria during tuber storage was noted. Full article
(This article belongs to the Section Agricultural Product Quality and Safety)
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<p>The process of ozonation of radish plants cultivated in the ground using a cover in the form of a foil tunnel.</p> Full article ">Figure 2
<p>Water content in samples subjected to ozonation and in the control sample on subsequent storage dates of radish tubers (<span class="html-italic">n</span> = 3). Small letters—differences between test dates; capital letters—differences between ozone doses.</p> Full article ">Figure 3
<p>Average values of destructive force for radish tubers depending on the ozone dose used and storage time (<span class="html-italic">n</span> = 3). Lowercase letters indicate differences between variants with the test date. Capital letters indicate differences between test dates.</p> Full article ">Figure 4
<p>Content of ascorbic acid in radish tubers depending on the ozone dose and storage time (<span class="html-italic">n</span> = 3). Lowercase letters indicate differences between variants with the test date. Capital letters indicate differences between test dates.</p> Full article ">Figure 5
<p>Antioxidant activity. ABTS test (<b>A</b>) and DPPH test (<b>B</b>) in radish tubers depending on the ozone dose and storage time (<span class="html-italic">n</span> = 3). Lowercase letters indicate differences between variants with the test date. Capital letters indicate differences between test dates.</p> Full article ">Figure 6
<p>Total content of polyphenols in radish tubers depending on the ozone dose and storage time (<span class="html-italic">n</span> = 3). Lowercase letters indicate differences between variants with the test date. Capital letters indicate differences between test dates.</p> Full article ">Figure 7
<p>Microbiological load. (<b>A</b>) Number of yeasts and molds; (<b>B</b>) number of aerobic mesophilic bacteria) of radish tubers depending on the ozone dose and storage time (<span class="html-italic">n</span> = 3). Lowercase letters indicate differences between variants with the test date. Capital letters indicate differences between test dates.</p> Full article ">
15 pages, 688 KiB  
Article
Mixed Silage of Banana Pseudostem and Maize Stover on Ethiopian Smallholder Farms: Effect of Fermentation Package and Location on Microbiological and Nutritional Evaluation
by Ashenafi Azage Mitiku, Dries Vandeweyer, Ines Adriaens, Yisehak Kechero, Leen Van Campenhout and Ben Aernouts
Agriculture 2023, 13(11), 2152; https://doi.org/10.3390/agriculture13112152 - 15 Nov 2023
Cited by 1 | Viewed by 1772
Abstract
Preservation of widely available crop residues as silage could reduce feed shortages in Ethiopia. Four mixtures of banana pseudostem (BPS) and fresh maize stover (FMS) were prepared for fermentation considering the local conditions and available resources: 100% FMS, 80% FMS + 20% BPS, [...] Read more.
Preservation of widely available crop residues as silage could reduce feed shortages in Ethiopia. Four mixtures of banana pseudostem (BPS) and fresh maize stover (FMS) were prepared for fermentation considering the local conditions and available resources: 100% FMS, 80% FMS + 20% BPS, 60% FMS + 40% BPS and 95% BPS + 5% molasses. Each of the four mixtures was fermented in plastic bags as well as in plastic drums. Apart from the effect of the mixture and fermentation package, two fermentation locations were also considered. The fermentation was replicated three times for each combination of mixture, fermentation package and fermentation condition. The pH, microbial counts (total viable count, lactic acid bacteria count, Enterobacteriaceae count, yeast and mold count) and nutritional values of the fresh material and mixed silage were measured. Fermentation was successful for all mixed silages, reaching a pH below four, while the total viable count, Enterobacteriaceae count, yeast and mold count dropped (all p ≤ 0.05) and digestibility and metabolizable energy increased compared to the fresh mixtures. Enterobacteriaceae counts reached values below the detection limit in all mixed silages fermented in drums unlike the bag silages. The plastic bags used as fermentation package were found to be sensitive to damage, resulting in a a higher pH and visible signs of yeast and mold. Although fermentation of BPS with molasses resulted in a significant increase in dry matter digestibility (41.14 to 46.17–49.92%) and organic matter digestibility (50.52 to 55.22–58.75%), they were lower compared to most mixed silages with FMS. Fermentation of 80 and 60% FMS mixtures increased the crude protein content from 44.30 to 71.27–82.20 g/kg DM, and from 43.63 to 63.10–65.83 g/kg DM, respectively. The highest increase (1.77 MJ/kg DM) in metabolizable energy was recorded for 80% FMS fermented in drums. The location of fermentation had no effect on pH, microbial counts and nutritional values. This study demonstrates that crop by-products can be successfully fermented under conditions prevailing in Ethiopia, with drums being preferred over bags. Mixing BPS with FMS is advised to absorb BPS juice losses and obtain silage with more crude protein, neutral and acid detergent fibers and metabolizable energy, as well as a higher digestibility. Full article
(This article belongs to the Special Issue Livestock Nutrition: Pasture System and Forage Conservation)
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<p>Mixed silage fermented 40% banana pseudostem and 60% fresh maize stover using bags (<b>left</b>) and drums (<b>right</b>).</p> Full article ">Figure 2
<p>Non-metric multidimensional scaling (NMDS, stress value 0.068) of the mean nutritional values for each mixture before fermentation (red symbols) and for each combination of mixture and fermentation package after fermentation. The mixtures are 100FMS (100% FMS), 80FMS (80% FMS + 20% BPS), 60FMS (60% FMS + 40% BPS) and 95BPS (95% BPS + 5% molasses), whereas the fermentation packages were bags (blue symbols) and drums (green symbols). The red symbols indicate the 4 mixtures before fermentation, while the blue symbols are the mixtures fermented in bags and the green symbols. Samples from the same mixture are displayed with the same symbol.</p> Full article ">
20 pages, 823 KiB  
Article
The Impact of Agricultural Global Value Chain Participation on Agricultural Total Factor Productivity
by Defeng Zhang and Zhilu Sun
Agriculture 2023, 13(11), 2151; https://doi.org/10.3390/agriculture13112151 - 15 Nov 2023
Cited by 3 | Viewed by 2503
Abstract
Under the condition of opening up, participation in international specialization and global value chains (GVCs) has become the main source for more and more countries to obtain foreign resources and advanced technologies, thereby promoting productivity improvement and technological progress. What are the pathways [...] Read more.
Under the condition of opening up, participation in international specialization and global value chains (GVCs) has become the main source for more and more countries to obtain foreign resources and advanced technologies, thereby promoting productivity improvement and technological progress. What are the pathways of agricultural GVC participation that affect agricultural total factor productivity (TFP)? Is the impact of agricultural GVC participation on agricultural TFP consistent across different statuses and modes of agricultural GVC participation? This paper elaborates on the theoretical mechanism of agricultural GVC participation affecting agricultural TFP, and then empirically estimates the impact of different statuses and modes of agricultural GVC participation on agricultural TFP by taking 58 countries as examples. The results show that agricultural GVCs affect agricultural TFP by several direct and indirect pathways. There was a U-shaped relationship between agricultural GVC participation and agricultural TFP, which means that after crossing a certain threshold, the former has a positive impact on the latter. By participating in agricultural GVCs, agricultural TFP in high-income and upper-middle-income countries was significantly improved, while in lower-middle-income countries it was not. Both forward and backward agricultural GVC participation were conducive to improving agricultural TFP in high-income and upper-middle-income countries, but only backward agricultural GVC participation was conducive to improving agricultural TFP in lower-middle-income countries. Therefore, every country needs to actively explore its optimal pathway to participate in agricultural GVCs in order to maximize the participation benefits and promote the improvement in agricultural TFP, simultaneously. Full article
(This article belongs to the Special Issue Agricultural Markets and Agrifood Supply Chains)
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<p>Impact pathways of agricultural global value chain (GVC) participation on agricultural total factor productivity (TFP) and corresponding research hypotheses. Note: FDI denotes foreign direct investment.</p> Full article ">Figure 2
<p>The kernel density distribution of agricultural GVC participation of 58 sample countries.</p> Full article ">
19 pages, 812 KiB  
Article
Exploring the Drivers of Microregional Agricultural Labor Productivity: Empirical Insights from Portugal
by Isabel Dinis
Agriculture 2023, 13(11), 2150; https://doi.org/10.3390/agriculture13112150 - 15 Nov 2023
Viewed by 1744
Abstract
Understanding the factors that influence agricultural productivity is critical for promoting sustainable food production, economic growth, and rural livelihoods. Despite the fact that numerous theoretical and empirical studies on agricultural productivity have been conducted in recent decades, few have focused on the local [...] Read more.
Understanding the factors that influence agricultural productivity is critical for promoting sustainable food production, economic growth, and rural livelihoods. Despite the fact that numerous theoretical and empirical studies on agricultural productivity have been conducted in recent decades, few have focused on the local geographical level, investigating the impact of specific agroecological conditions and farming systems. The current study examines the geographical micro-level determinants of labor productivity for all farmers and agricultural holdings in Portugal by estimating the parameters of an extended Cobb–Douglas production function and using panel data techniques. In general, the findings support major findings in empirical and theoretical literature that show a positive relationship between labor productivity and farm size, mechanization, irrigation, and human capital. Labor productivity is higher in regions with a higher prevalence of Mediterranean farming systems, such as orchards, vineyards, and horticultural crops, possibly due to crop suitability and ancient specialized knowledge, implying that a shift in farming techniques and crop selection, in balance with local natural and social specificities, may increase agricultural output and income for rural communities. Full article
(This article belongs to the Section Agricultural Economics, Policies and Rural Management)
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<p>Portuguese agrarian regions [<a href="#B13-agriculture-13-02150" class="html-bibr">13</a>].</p> Full article ">
19 pages, 2902 KiB  
Article
Can Modification of Sowing Date and Genotype Selection Reduce the Impact of Climate Change on Sunflower Seed Production?
by Miloš Krstić, Velimir Mladenov, Borislav Banjac, Brankica Babec, Dušan Dunđerski, Nemanja Ćuk, Sonja Gvozdenac, Sandra Cvejić, Siniša Jocić, Vladimir Miklič and Jelena Ovuka
Agriculture 2023, 13(11), 2149; https://doi.org/10.3390/agriculture13112149 - 15 Nov 2023
Cited by 4 | Viewed by 1819
Abstract
Climate change projections for the 21st century pose great threats to semi-arid regions, impacting seed production and the quality of sunflowers. Crop yields are negatively affected by climate variability, especially in the event of droughts during the crucial growth stages. Understanding the relationships [...] Read more.
Climate change projections for the 21st century pose great threats to semi-arid regions, impacting seed production and the quality of sunflowers. Crop yields are negatively affected by climate variability, especially in the event of droughts during the crucial growth stages. Understanding the relationships between agrometeorological, genetic, and agronomic factors is crucial for maintaining crop sustainability. Optimal sowing dates are an essential condition for maximizing crop genetic potential, but challenges come from annual weather variations. This study analyzes how sunflower genotypes respond to different sowing dates under climate change and focuses on the conditions for obtaining maximum seed yields and favorable agronomic traits. From 2020 to 2022, the experiment featured six genotypes sown across four different dates at two-week intervals, simulating seed sunflower production. The results obtained by ANOVA indicated that the seed yield and oil yield were significantly affected by the sowing date, the genotype, and their interaction, with coefficients of variation ranging from 7.6% for oil yield to 41.1% for seed yield. Besides seed yield and oil yield, LDA biplot and Discriminant Functions confirmed that seed germination energy also played a significant role in separating genotypes into clusters. A Visual Mixed Model showed that shifting the optimal sowing date (mid-April) to early May allows a reduction in the number of days the plants spend in critical growth stages, thereby escaping stressful conditions during pollination and seed filling. The findings resulted, on average, in increased yields and improved seed quality, which are the primary goals of seed production, but not in increased 1000-seed weight. Notably, high temperatures during the critical sunflower growth stages negatively affected the measured parameters of seed production. The increased precipitation during seed filling boosted the 1000-seed mass and seed yield. Extended flowering reduced the growth rate and seed germination, but longer seed filling increased the 1000-seed mass and seed yield. Our future breeding goals will be to create genotypes with a shorter flowering period and an extended seed-filling period to better respond to climate change. Full article
(This article belongs to the Section Seed Science and Technology)
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<p>Sum of precipitation and average temperatures for the multi-year average and three years of the experiment at the location of Rimski šančevi, Serbia.</p> Full article ">Figure 2
<p>Linear discriminant analysis (LDA). (<b>a</b>) Interaction of genotype and first sowing date; (<b>b</b>) interaction of genotype and second sowing date; (<b>c</b>) interaction of genotype and third sowing date; (<b>d</b>) interaction of genotype and fourth sowing date. Biplot graphs where two dimensions (Canonical1, i.e., DF1, and Canonical2, i.e., DF2) provide maximum separation of genotypes into groups based on dependent variables in the experiment. Around the center of each group (+sign), there is a circle representing the 95% confidence interval. These circles do not overlap when groups differ significantly from each other statistically. If the circles overlap, it indicates that those genotypes form one cluster. Genotype G1 is marked with red circles; genotype G2 is marked with green crosses; genotype G3 is marked with blue diamonds; genotype G4 is marked with orange letter X; genotype G5 is marked with green triangles; genotype G6 is marked with purple letter Y.</p> Full article ">Figure 3
<p>Visual Mixed Model (VMM). (<b>a</b>) The influence of average daily temperature during flowering on GR depending on sowing dates; (<b>b</b>) the influence of average daily temperature during seed filling on GR depending on sowing dates; (<b>c</b>) the influence of sum of precipitation during flowering on GR depending on sowing dates; (<b>d</b>) the influence of sum of precipitation during seed filling on GR depending on sowing dates; (<b>e</b>) the influence of number of flowering days on GR depending on sowing dates; (<b>f</b>) the influence of number days of seed filling on GR depending on sowing dates. AT R5-R6—average daily temperature during flowering; AT R6-R9*—average daily temperature during seed filling; SP R5-R6—sum of precipitation during flowering; SP R6-R9*—sum of precipitation during seed filling; ND R5-R6—number of flowering days; ND R6-R9*—number of days of seed filling; GS—growing season; GR—seed germination.</p> Full article ">Figure 4
<p>Visual Mixed Model (VMM). (<b>a</b>) The influence of average daily temperature during flowering on TSM depending on sowing dates; (<b>b</b>) the influence of average daily temperature during seed filling on TSM depending on sowing dates; (<b>c</b>) the influence of sum of precipitation during flowering on TSM depending on sowing dates; (<b>d</b>) the influence of sum of precipitation during seed filling on TSM depending on sowing dates; (<b>e</b>) the influence of number of flowering days on TSM depending on sowing dates; (<b>f</b>) the influence of number days of seed filling on TSM depending on sowing dates. AT R5-R6—average daily temperature during flowering; AT R6-R9*—average daily temperature during seed filling; SP R5-R6—sum of precipitation during flowering; SP R6-R9*—sum of precipitation during seed filling; ND R5-R6—number of flowering days; ND R6-R9*—number of days of seed filling; GS—growing season; GR—seed germination.</p> Full article ">Figure 5
<p>Visual Mixed Model (VMM). (<b>a</b>) The influence of average daily temperature during flowering on Y depending on sowing dates; (<b>b</b>) the influence of average daily temperature during seed filling on Y depending on sowing dates; (<b>c</b>) the influence of sum of precipitation during flowering on Y depending on sowing dates; (<b>d</b>) the influence of sum of precipitation during seed filling on Y depending on sowing dates; (<b>e</b>) the influence of number of flowering days on Y depending on sowing dates; (<b>f</b>) the influence of number days of seed filling on Y depending on sowing dates. AT R5-R6—average daily temperature during flowering; AT R6-R9*—average daily temperature during seed filling; SP R5-R6—sum of precipitation during flowering; SP R6-R9*—sum of precipitation during seed filling; ND R5-R6—number of flowering days; ND R6-R9*—number of days of seed filling; GS—growing season; GR—seed germination.</p> Full article ">
12 pages, 3272 KiB  
Article
Soil Properties of Fallow Land Invaded by Black Cherry (Padus serotina (Ehrh.) Borkh.)
by Paulina Bączek, Aleksandra Halarewicz, Daniel Pruchniewicz, Magda Podlaska and Dorota Kawałko
Agriculture 2023, 13(11), 2148; https://doi.org/10.3390/agriculture13112148 - 14 Nov 2023
Viewed by 1387
Abstract
The extensive spread of the invasive black cherry, Padus serotina, has been observed on abandoned agricultural land in Central and Eastern Europe. However, the impact of this species on invaded agroecosystems is still unknown, including the possibility of returning these ecosystems to [...] Read more.
The extensive spread of the invasive black cherry, Padus serotina, has been observed on abandoned agricultural land in Central and Eastern Europe. However, the impact of this species on invaded agroecosystems is still unknown, including the possibility of returning these ecosystems to agricultural production. In order to evaluate the selected soil properties of fallows invaded by P. serotina, their texture, field water capacity, reaction, and content of organic carbon, total nitrogen, and available forms of potassium and phosphorus were determined for 100 study plots. Taking into account the influence of soil conditions on floristic composition, the area covered by individual plant species in the study plots was also included in the analysis. A relationship was found between the presence of all the developmental stages of P. serotina and an increase in the phosphorus content in the soil. With the growth of a black cherry shrub layer, the content of soil nitrogen and potassium increased. An increasing proportion of P. serotina in the herb layer contributed to soil acidification and reduced the water content available for plants in the arable layer at 20–40 cm. The possible impact of P. serotina on soil properties may be an additional premise when considering the possibilities and benefits of the recultivation of fallow land invaded by this species. Full article
(This article belongs to the Section Agricultural Soils)
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<p>Location of study sites with their numbering (1–10) in accordance with <a href="#agriculture-13-02148-t001" class="html-table">Table 1</a>.</p> Full article ">Figure 2
<p>Canonical correspondence analysis (CCA) diagram for the studied plots and soil variables, represented as vectors in the ordination space. Plots with <span class="html-italic">Padus serotina</span> are marked as red points, and plots without this species are marked as black points. Numbers of plots are as described in <a href="#agriculture-13-02148-t001" class="html-table">Table 1</a>. Designations of soil variables are given in the description of <a href="#agriculture-13-02148-t002" class="html-table">Table 2</a>.</p> Full article ">Figure 3
<p>Canonical correspondence analysis (CCA) diagram for the species of the herb and moss layer. The letter “d” before the abbreviation denotes moss species. Species names and abbreviations: <span class="html-italic">Ace_ne</span>—<span class="html-italic">Acer negundo</span>, <span class="html-italic">Ach_mi</span>—<span class="html-italic">Achillea millefolium</span>, <span class="html-italic">Agr_re</span>—<span class="html-italic">Agropyron repens</span>, <span class="html-italic">Alo_pr</span>—<span class="html-italic">Alopecurus pratensis</span>, <span class="html-italic">Ant_od</span>—<span class="html-italic">Anthoxanthum odoratum</span>, <span class="html-italic">Ant_sy</span>—<span class="html-italic">Anthriscus sylvestris</span>, <span class="html-italic">Arr_el</span>—<span class="html-italic">Arrhenatherum elatius</span>, <span class="html-italic">dAtr_un</span>—<span class="html-italic">Atrichum undulatum</span>, <span class="html-italic">Bet_pe</span>—<span class="html-italic">Betula pendula</span>, <span class="html-italic">dBra_al</span>—<span class="html-italic">Brachythecium albicans</span>, <span class="html-italic">dBra_oe</span>—<span class="html-italic">Brachythecium oedipodium</span>, <span class="html-italic">dBra_ru</span>—<span class="html-italic">Brachythecium rutabulum</span>, <span class="html-italic">Cal_ep</span>—<span class="html-italic">Calamagrostis epigejos</span>, <span class="html-italic">Cam_pa</span>—<span class="html-italic">Campanula patula</span>, <span class="html-italic">Car_sp</span>—<span class="html-italic">Cardaminopsis sp.</span>, <span class="html-italic">Car_ac</span>—<span class="html-italic">Carduus acanthoides</span>, <span class="html-italic">Car_hi</span>—<span class="html-italic">Carex hirta</span>, <span class="html-italic">Car_vu</span>—<span class="html-italic">Carlina vulgaris</span>, <span class="html-italic">Car_ca</span>—<span class="html-italic">Carum carvi</span>, <span class="html-italic">dCer_pu</span>—<span class="html-italic">Ceratodon purpureus</span>, <span class="html-italic">Cir_ ar</span>—<span class="html-italic">Cirsium arvense</span>, <span class="html-italic">Cir_vu</span>—<span class="html-italic">Cirsium vulgare</span>, <span class="html-italic">Con_ca</span>—<span class="html-italic">Conyza canadensis</span>, <span class="html-italic">Cor_sa</span>—<span class="html-italic">Cornus sanguinea</span>, <span class="html-italic">Cra_mo</span>—<span class="html-italic">Crataegus monogyna</span>, <span class="html-italic">Cre_bi</span>—<span class="html-italic">Crepis biennis</span>, <span class="html-italic">Cre_te</span>—<span class="html-italic">Crepis tectorum</span>, <span class="html-italic">Dac_gl</span>—<span class="html-italic">Dactylis glomerata</span>, <span class="html-italic">Dia_de</span>—<span class="html-italic">Dianthus deltoides</span>, <span class="html-italic">Dry_fi</span>—<span class="html-italic">Dryopteris filix–mas</span>, <span class="html-italic">Equ_pr</span>—<span class="html-italic">Equisetum pratense</span>, <span class="html-italic">Eup_cy</span>—<span class="html-italic">Euphorbia cyparissias</span>, <span class="html-italic">dEur_hi</span>—<span class="html-italic">Eurhynchium hians</span>, <span class="html-italic">Fal_du</span>—<span class="html-italic">Fallopia dumetorum</span>, <span class="html-italic">Fes_pr</span>—<span class="html-italic">Festuca pratensis</span>, <span class="html-italic">Fes_ru</span>—<span class="html-italic">Festuca rubra</span>, <span class="html-italic">Fes_sp</span>—<span class="html-italic">Festuca sp.</span>, <span class="html-italic">Fra_ve</span>—<span class="html-italic">Fragaria vesca</span>, <span class="html-italic">Fra_ex</span>—<span class="html-italic">Fraxinus excelsior</span>, <span class="html-italic">Gal_ve</span>—<span class="html-italic">Galium verum</span>, <span class="html-italic">Ger_pu</span>—<span class="html-italic">Geranium pusillum</span>, <span class="html-italic">Geu_ur</span>—<span class="html-italic">Geum urbanum</span>, <span class="html-italic">Gna_sy</span>—<span class="html-italic">Gnaphalium sylvaticum</span>, <span class="html-italic">Her_sp</span>—<span class="html-italic">Heracleum sphondylium</span>, <span class="html-italic">Hie_pi</span>—<span class="html-italic">Hieracium pilosella</span>, <span class="html-italic">Hie_um</span>—<span class="html-italic">Hieracium umbellatum</span>, <span class="html-italic">Ho_la</span>—<span class="html-italic">Holcus lanatus</span>, <span class="html-italic">Hol_mo</span>—<span class="html-italic">Holcus mollis</span>, <span class="html-italic">Hyp_pe</span>—<span class="html-italic">Hypericum perforatum</span>, <span class="html-italic">Hyp_ra</span>—<span class="html-italic">Hypochoeris radicata</span>, <span class="html-italic">Jas_mo</span>—<span class="html-italic">Jasione montana</span>, <span class="html-italic">Lac_se</span>—<span class="html-italic">Lactuca serriola</span>, <span class="html-italic">Lat_pr</span>—<span class="html-italic">Lathyrus pratensis</span>, <span class="html-italic">Lat_tu</span>—<span class="html-italic">Lathyrus tuberosus</span>, <span class="html-italic">Lon_xy</span>—<span class="html-italic">Lonicera xylosteum</span>, <span class="html-italic">Luz_ca</span>—<span class="html-italic">Luzula campestris</span>, <span class="html-italic">Oxa_st</span>—<span class="html-italic">Oxalis stricta</span>, <span class="html-italic">Pad_av</span>—<span class="html-italic">Padus avium</span>, <span class="html-italic">Pad_se</span>—<span class="html-italic">Padus serotina</span>, <span class="html-italic">Pap_du</span>—<span class="html-italic">Papaver dubium</span>, <span class="html-italic">Pic_hi</span>—<span class="html-italic">Picris hieracioides</span>, <span class="html-italic">Pim_ma</span>—<span class="html-italic">Pimpinella major</span>, <span class="html-italic">Pla_la</span>—<span class="html-italic">Plantago lanceolata</span>, <span class="html-italic">Poa_pr</span>—<span class="html-italic">Poa pratensis</span>, <span class="html-italic">Pru_do</span>—<span class="html-italic">Prunus domestica</span>, <span class="html-italic">Pru_do_s</span>—<span class="html-italic">Prunus domestica subsp. syriaca</span>, <span class="html-italic">Que_ro</span>—<span class="html-italic">Quercus robur</span>, <span class="html-italic">Ros_sp</span>—<span class="html-italic">Rosa sp.</span>, <span class="html-italic">Ru_ca</span>—<span class="html-italic">Rubus caesius</span>, <span class="html-italic">Rub_sp</span>—<span class="html-italic">Rubus sp.</span>, <span class="html-italic">Rum_ac</span>—<span class="html-italic">Rumex acetosa</span>, <span class="html-italic">Sen_ja</span>—<span class="html-italic">Senecio jacobaea</span>, <span class="html-italic">Sol_ca</span>—<span class="html-italic">Solidago canadensis</span>, <span class="html-italic">Sol_gi</span>—<span class="html-italic">Solidago gigantea</span>, <span class="html-italic">Sta_pa</span>—<span class="html-italic">Stachys palustris</span>, <span class="html-italic">Ste_me</span>—<span class="html-italic">Stellaria media</span>, <span class="html-italic">Sym_of</span>—<span class="html-italic">Symphytum officinale</span>, <span class="html-italic">Syr_vu</span>—<span class="html-italic">Syringa vulgaris</span>, <span class="html-italic">Tor_ja</span>—<span class="html-italic">Torilis japonica</span>, <span class="html-italic">Tri_ar</span>—<span class="html-italic">Trifolium arvense</span>, <span class="html-italic">Tri_du</span>—<span class="html-italic">Trifolium dubium</span>, <span class="html-italic">Ulm_mi</span>—<span class="html-italic">Ulmus minor</span>, <span class="html-italic">Val_of</span>—<span class="html-italic">Valeriana officinalis</span>, <span class="html-italic">Ver_th</span>—<span class="html-italic">Verbascum thapsus</span>, <span class="html-italic">Ver_ch</span>—<span class="html-italic">Veronica chamaedrys</span>, <span class="html-italic">Ver_of</span>—<span class="html-italic">Veronica officinalis</span>, <span class="html-italic">Vic_cr</span>—<span class="html-italic">Vicia cracca</span>, <span class="html-italic">Vic_gr</span>—<span class="html-italic">Vicia grandiflora</span>.</p> Full article ">
12 pages, 2726 KiB  
Article
A New Protocol to Mitigate Damage to Germination Caused by Black Layers in Maize (Zea mays L.) Seeds
by Joon Ki Hong, Jeongho Baek, Sang Ryeol Park, Gang Seob Lee and Eun Jung Suh
Agriculture 2023, 13(11), 2147; https://doi.org/10.3390/agriculture13112147 - 14 Nov 2023
Cited by 1 | Viewed by 1950
Abstract
Maize seeds harvested in the field have higher vitality than those harvested in greenhouses but have higher contamination rates in terms of fungal or bacterial infection. It is important to disinfect maize seeds before sowing because seeds are a source of infection and [...] Read more.
Maize seeds harvested in the field have higher vitality than those harvested in greenhouses but have higher contamination rates in terms of fungal or bacterial infection. It is important to disinfect maize seeds before sowing because seeds are a source of infection and damage crop production. In this study, we aimed to provide an efficient seed sterilization method to manage fungal or bacterial infections of field-harvested maize seeds. The optimized sterilization protocol was set up according to the disinfectant liquid immersion time, inverting RPM (rotations per minute), number of seeds, and black layer removal. We put 20 grains of maize seeds in 100% commercial bleach disinfectant containing 4–5% NaClO and performed 20 min of inversion at 45 RPM. After standing without inverting for the next 25 min in the sterile hood, inversion at 45 RPM for another 40 min was performed. By using this protocol, microorganisms occurred at a low rate with an average of 11.7%. Moreover, it was shown that microorganisms occurred at the lowest rate (average of 0.29% of seeds) when the black layer was removed. In addition, this sterilization method did not affect the growth and development of maize plants. These results revealed that black layer removal from maize seeds is a highly efficient, easy, and inexpensive sterilization method and can be used for seeds of various maize lines. Full article
(This article belongs to the Section Agricultural Technology)
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<p>Microbial growth on maize Hi IIA seeds exposed to different sterilization treatments. Images show microbial growth on seeds exposed to sterilization treatments and grown on the basal medium for 4 days. Bar = 1 cm.</p> Full article ">Figure 2
<p>Microbial growth on maize Hi IIA seeds exposed to different sterilization treatments after removal of the black layer of maize seeds. Macroscopic images show microbial growth on seeds exposed to sterilization treatments and grown on the basal medium for 4 days. Bar = 1 cm.</p> Full article ">Figure 3
<p>Microbial growth with changing inversion RPM and different numbers of seeds during seed sterilization according to the method of Treatment 4 after removal of the black layer of maize in Hi IIA seeds. Photographs show the growth of microorganisms on seeds subjected to different RPM (<b>A</b>) and with differing numbers of seeds (<b>B</b>) during sterilization treatment (seeds were grown on basal medium for 4 days). Bars = 1 cm.</p> Full article ">
23 pages, 1515 KiB  
Article
Effects of Silicon Alone and Combined with Organic Matter and Trichoderma harzianum on Sorghum Yield, Ions Accumulation and Soil Properties under Saline Irrigation
by José Orlando Nunes da Silva, Luiz Guilherme Medeiros Pessoa, Emanuelle Maria da Silva, Leonardo Raimundo da Silva, Maria Betânia Galvão dos Santos Freire, Eduardo Soares de Souza, Sérgio Luiz Ferreira-Silva, José Geraldo Eugênio de França, Thieres George Freire da Silva and Eurico Lustosa do Nascimento Alencar
Agriculture 2023, 13(11), 2146; https://doi.org/10.3390/agriculture13112146 - 14 Nov 2023
Cited by 1 | Viewed by 1941
Abstract
The action of silicon as a salt stress mitigator has been investigated in isolation, and its combined efficacy with other salt stress mitigators needs to be addressed. This work verified whether silicon, in combination with organic matter and Trichoderma harzianum, enhances the [...] Read more.
The action of silicon as a salt stress mitigator has been investigated in isolation, and its combined efficacy with other salt stress mitigators needs to be addressed. This work verified whether silicon, in combination with organic matter and Trichoderma harzianum, enhances the production of forage sorghum under saline irrigation and its effects on soil properties. The field experiment was conducted in Parnamirim (PE), a semiarid region of Brazil. Forage sorghum (Sorghum sudanense (Piper) Stapf) was irrigated with saline water (3.12 dS m−1) and subjected to the application of non-silicon, silicon alone, and silicon combined with Trichoderma and organic matter over three consecutive cuts (every three months after germination). Silicon applied in combination significantly increased the content of nutrient ions K+, P, Ca2+, and Mg2+ in sorghum leaves, stems, and panicles and increased P content in the soil by 170, 288, and 92% for the first, second, and third cuts, respectively. When silicon was applied in combination, sorghum’s dry and fresh matter (total yield for the three cuts) increased to 62.53 and 182.43 t ha−1, respectively. In summary, applying silicon (Si) combined with Trichoderma and organic matter promotes higher nutrient ion contents in soil and sorghum plants and a higher forage sorghum yield. Full article
(This article belongs to the Special Issue Agricultural Crops Subjected to Drought and Salinity Stress)
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<p>Location of the experimental field in Parnamirim (PE), a semiarid region of Brazil.</p> Full article ">Figure 2
<p>Meteorological conditions in the municipality of Parnamirim (PE) during the experimental period. ID—irrigation depth (mm day<sup>−1</sup>); ETo—reference evapotranspiration (mm day<sup>−1</sup>).</p> Full article ">Figure 3
<p>Mean values of the saline variables: electrical conductivity—EC (<b>A</b>), soil pH (<b>B</b>), and exchangeable sodium percent—ESP (<b>C</b>) at depths of 0–10, 10–20, 20–40, and 40–60 cm for each cut, depending on the tested saline attenuator. Different letters indicate significant differences at <span class="html-italic">p</span> ≤ 0.05. Si = silicon; Si + OM = silicon + organic matter; Si + T = silicon + <span class="html-italic">Trichoderma harzianum</span>; Si + OM + T = silicon + organic matter + <span class="html-italic">Trichoderma harzianum</span>.</p> Full article ">
15 pages, 1113 KiB  
Article
Usefulness of Living Mulch in Rows in a Dwarf Pear, Pyrus communis L., Orchard
by Ireneusz Sosna and Ewa Fudali
Agriculture 2023, 13(11), 2145; https://doi.org/10.3390/agriculture13112145 - 14 Nov 2023
Cited by 1 | Viewed by 1115
Abstract
The key problem in the cultivation of densely planted dwarf orchards is the removal of weeds—trees’ competitors for habitat resources. There is an urgent need to look for ecological methods of weed control as an alternative to herbicides that are harmful to the [...] Read more.
The key problem in the cultivation of densely planted dwarf orchards is the removal of weeds—trees’ competitors for habitat resources. There is an urgent need to look for ecological methods of weed control as an alternative to herbicides that are harmful to the environment. The use of living mulch (LM) in tree rows additionally improves soil quality but usually weakens tree growth and may reduce yield. The aim of this 11-year experiment was to assess the impact of the use of two different LMs in rows (Trifolium repens—Tr and Agrostis capillaris—Ac) on the growth, yield, and fruit quality of three pear cultivars on Quince S1 rootstock compared to herbicide fallow. The presence of LM did not significantly affect tree growth. There was no significant effect of either mulch on the cumulative yield. However, for the first 4–6 years, the yield was clearly lower than in the control, which changed in the later years of the experiment. When LMs were used, pear trees showed a significantly lower tendency to alternate fruiting. The average fruit weight was significantly lower in Tr, but the other parameters of external fruit quality did not differ significantly. Furthermore, a smaller share of ultra-small fruit was found with LM compared to the control. The LM did not significantly affect such parameters as the content of soluble solids, vitamin C, Ca, Mg, and P. The use of Ac in dwarf pear orchards with sowing in tree rows is recommended in the 2nd or 3rd year after planting at the earliest. Full article
(This article belongs to the Special Issue Preharvest and Postharvest Factors Influencing Biological Active Compounds in Horticultural Commodities)
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<p>Yield of three pear cultivars (kg per tree) depending on orchard floor management in tree rows.</p> Full article ">Figure 2
<p>Influence of orchard floor management on the fruit size of three pear cultivars (mean for 2012–2016).</p> Full article ">Figure 3
<p>Influence of orchard floor management on the fruit colouring of three pear cultivars (mean for 2012–2016).</p> Full article ">
20 pages, 11140 KiB  
Article
Embedded Field Stalk Detection Algorithm for Digging–Pulling Cassava Harvester Intelligent Clamping and Pulling Device
by Wang Yang, Junhui Xi, Zhihao Wang, Zhiheng Lu, Xian Zheng, Debang Zhang and Yu Huang
Agriculture 2023, 13(11), 2144; https://doi.org/10.3390/agriculture13112144 - 14 Nov 2023
Cited by 2 | Viewed by 1574
Abstract
Cassava (Manihot esculenta Crantz) is a major tuber crop worldwide, but its mechanized harvesting is inefficient. The digging–pulling cassava harvester is the primary development direction of the cassava harvester. However, the harvester clamping–pulling mechanism cannot automatically adjust its position relative to the [...] Read more.
Cassava (Manihot esculenta Crantz) is a major tuber crop worldwide, but its mechanized harvesting is inefficient. The digging–pulling cassava harvester is the primary development direction of the cassava harvester. However, the harvester clamping–pulling mechanism cannot automatically adjust its position relative to the stalks in forward movement, which results in clamping stalks with a large off-center distance difficulty, causing large harvest losses. Thus, solving the device’s clamping location problem is the key to loss reduction in the harvester. To this end, this paper proposes a real-time detection method for field stalks based on YOLOv4. First, K-means clustering is applied to improve the consistency of cassava stalk detection boxes. Next, the improved YOLOv4 network’s backbone is replaced with MobileNetV2 + CA, resulting in the KMC-YOLO network. Then, the proposed model’s validity is demonstrated using ablation studies and comparison tests. Finally, the improved network is embedded into the NVIDIA Jetson AGX Xavier, and the model is accelerated using TensorRT, before conducting field trials. The results indicate that the KMC-YOLO achieves average precision (AP) values of 98.2%, with detection speeds of 33.6 fps. The model size is reduced by 53.08% compared with the original YOLOv4 model. The detection speed after TensorRT acceleration is 39.3 fps, which is 83.64% faster than before acceleration. Field experiments show that the embedded model detects more than 95% of the time at all three harvest illumination levels. This research contributes significantly to the development of cassava harvesters with intelligent harvesting operations. Full article
(This article belongs to the Special Issue Intelligent Agricultural Machinery and Robots: Embracing Technological Advancements for a Sustainable and Highly Efficient Agricultural Future)
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<p>Typical models in three types of mainstream cassava harvesters: (<b>a</b>) digging cassava harvester; (<b>b</b>) digging and shaking separation type cassava harvester; (<b>c</b>) digging–pulling cassava harvester.</p> Full article ">Figure 2
<p>The growth of cassava stalks.</p> Full article ">Figure 3
<p>Cassava planting patterns and cassava stalk distribution during harvest.</p> Full article ">Figure 4
<p>Situation of cassava stalks cut off before harvesting.</p> Full article ">Figure 5
<p>Field image acquisition platform.</p> Full article ">Figure 6
<p>Cassava stalk truncated cross-section (test section).</p> Full article ">Figure 7
<p>Detection of light intensity: (<b>a</b>) field; (<b>b</b>) indoor.</p> Full article ">Figure 8
<p>Indoor field image dynamic simulation acquisition platform.</p> Full article ">Figure 9
<p>Data amplification method: (<b>a</b>) motion blur; (<b>b</b>) geometric rotation; (<b>c</b>) impulse noise; (<b>d</b>) Gaussian noise; (<b>e</b>) elastic transform; (<b>f</b>) random erasing (Black square is filled with zero pixel values).</p> Full article ">Figure 10
<p>KMC-YOLO network structure: conv refers to convolution; SPP is spatial pyramid pooling; CA is coordinate attention; Conv is convolutional layer.</p> Full article ">Figure 11
<p>Development environment configuration.</p> Full article ">Figure 12
<p>KMC-YOLO training process: the term total−loss refers to the overall loss, loss−ciou represents the CIoU loss, loss−conf represents the confidence loss, and loss−class represents the classification loss.</p> Full article ">Figure 13
<p>KMC-YOLO test results.</p> Full article ">Figure 14
<p>Images of the testing process: (<b>a</b>) low light detection screen in the morning; (<b>b</b>) high light detection screen at noon; (<b>c</b>) low light detection screen in the evening.</p> Full article ">
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