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32 pages, 49926 KiB  
Article
What Are the Variation Patterns of Vegetation and Its Influencing Factors in China from 2000–2020 from the Partition Perspective?
by Bing Guo, Mei Xu, Rui Zhang, Wei Luo and Jicun Yang
Forests 2024, 15(8), 1409; https://doi.org/10.3390/f15081409 (registering DOI) - 11 Aug 2024
Abstract
China’s vegetation ecosystem has undergone profound changes, and there is an urgent need to explore the mechanisms behind vegetation changes in different ecological sub-regions and historical periods across China. Based on NDVI (normalized difference vegetation index) data, this study analyzed the spatial and [...] Read more.
China’s vegetation ecosystem has undergone profound changes, and there is an urgent need to explore the mechanisms behind vegetation changes in different ecological sub-regions and historical periods across China. Based on NDVI (normalized difference vegetation index) data, this study analyzed the spatial and temporal evolution patterns and driving mechanisms of six ecological sub-regions in China. The results showed that: (1) over the past 20 years, the vegetation coverage in mainland China showed a decreasing trend from east to west. (2) Over the past 20 years, the vegetation coverage of the six ecological sub-regions showed an increasing trend, with the highest increase in Central Southern China (0.0039) and the lowest increase in East China (0.002). (3) The gravity center of Northeast China showed a trend of migration to the northwest. The gravity center of North China, East China, and Central South China showed a trend of migration to the southwest, while that of Northwest and Southwest China showed a trend of migration to the southeast. (4) During the period from 2000 to 2020, vegetation cover levels showed an upward trend. (5) The lag time of vegetation types in different regions was different. (6) Precipitation was the dominant influencing factor in the evolution of vegetation in Northeast China, North China, Northwest China, and Southwest China. The dominant influencing factors of vegetation evolution in East China were land use and GDP (gross domestic product), while the dominant influencing factors of vegetation evolution in Central South China were precipitation and land use. Full article
(This article belongs to the Special Issue Monitoring Forest Change Dynamic with Remote Sensing)
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Figure 1

Figure 1
<p>Overview of study area.</p>
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<p>The distribution of meteorological stations in China.</p>
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<p>Spatial distributions of different levels of NDVI.</p>
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<p>Interannual variation of NDVI in different regions: (<b>a</b>) Northeast China, (<b>b</b>) North China, (<b>c</b>) East China, (<b>d</b>) Northwest China, (<b>e</b>) Southwest China, (<b>f</b>) Central–South China.</p>
Full article ">Figure 4 Cont.
<p>Interannual variation of NDVI in different regions: (<b>a</b>) Northeast China, (<b>b</b>) North China, (<b>c</b>) East China, (<b>d</b>) Northwest China, (<b>e</b>) Southwest China, (<b>f</b>) Central–South China.</p>
Full article ">Figure 4 Cont.
<p>Interannual variation of NDVI in different regions: (<b>a</b>) Northeast China, (<b>b</b>) North China, (<b>c</b>) East China, (<b>d</b>) Northwest China, (<b>e</b>) Southwest China, (<b>f</b>) Central–South China.</p>
Full article ">Figure 5
<p>The gravity center of different partitions year by year: (<b>a1</b>) Northeast China (space); (<b>a2</b>) Northeast China (polar coordinates); (<b>b1</b>) North China (space); (<b>b2</b>) North China (polar coordinates); (<b>c1</b>) East China (space); (<b>c2</b>) East China (polar coordinates); (<b>d1</b>) Northwest China (space); (<b>d2</b>) Northwest China (polar coordinates); (<b>e1</b>) Southwest China (space); (<b>e2</b>) Southwest China (polar coordinates); (<b>f1</b>) Central–South China (space); (<b>f2</b>) Central–South China (polar coordinates).</p>
Full article ">Figure 5 Cont.
<p>The gravity center of different partitions year by year: (<b>a1</b>) Northeast China (space); (<b>a2</b>) Northeast China (polar coordinates); (<b>b1</b>) North China (space); (<b>b2</b>) North China (polar coordinates); (<b>c1</b>) East China (space); (<b>c2</b>) East China (polar coordinates); (<b>d1</b>) Northwest China (space); (<b>d2</b>) Northwest China (polar coordinates); (<b>e1</b>) Southwest China (space); (<b>e2</b>) Southwest China (polar coordinates); (<b>f1</b>) Central–South China (space); (<b>f2</b>) Central–South China (polar coordinates).</p>
Full article ">Figure 5 Cont.
<p>The gravity center of different partitions year by year: (<b>a1</b>) Northeast China (space); (<b>a2</b>) Northeast China (polar coordinates); (<b>b1</b>) North China (space); (<b>b2</b>) North China (polar coordinates); (<b>c1</b>) East China (space); (<b>c2</b>) East China (polar coordinates); (<b>d1</b>) Northwest China (space); (<b>d2</b>) Northwest China (polar coordinates); (<b>e1</b>) Southwest China (space); (<b>e2</b>) Southwest China (polar coordinates); (<b>f1</b>) Central–South China (space); (<b>f2</b>) Central–South China (polar coordinates).</p>
Full article ">Figure 6
<p>Different partition migration routes: (<b>a</b>) Northeast China; (<b>b</b>) North China; (<b>c</b>) East China; (<b>d</b>) Northwest China; (<b>e</b>) Southwest China; (<b>f</b>) Central–South China.</p>
Full article ">Figure 6 Cont.
<p>Different partition migration routes: (<b>a</b>) Northeast China; (<b>b</b>) North China; (<b>c</b>) East China; (<b>d</b>) Northwest China; (<b>e</b>) Southwest China; (<b>f</b>) Central–South China.</p>
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<p>Changes of NDVI transfer at different grades in different time periods: (<b>a</b>) 2000–2010 and (<b>b</b>) 2011–2020.</p>
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<p>Changes of NDVI transfer at different grades in different time periods: (<b>a</b>) 2000–2010 and (<b>b</b>) 2011–2020.</p>
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<p>Spatial distribution of lag of time for different vegetation types. (<b>a1</b>) Precipitation (meadow; herbosa; grassland); (<b>a2</b>) precipitation (coniferous forest; broad-leaved forest; coniferous and broad-leaved forest); (<b>a3</b>) precipitation (alpine vegetation; cultural vegetation); (<b>a4</b>) precipitation (bush-wood; bog; desert; others); (<b>b1</b>) temperature (meadow; herbosa; grassland); (<b>b2</b>) temperature (coniferous forest; broad-leaved forest; coniferous and broad-leaved forest); (<b>b3</b>) temperature (alpine vegetation; cultural vegetation); (<b>b4</b>) temperature (bush-wood; bog; desert; others).</p>
Full article ">Figure 8 Cont.
<p>Spatial distribution of lag of time for different vegetation types. (<b>a1</b>) Precipitation (meadow; herbosa; grassland); (<b>a2</b>) precipitation (coniferous forest; broad-leaved forest; coniferous and broad-leaved forest); (<b>a3</b>) precipitation (alpine vegetation; cultural vegetation); (<b>a4</b>) precipitation (bush-wood; bog; desert; others); (<b>b1</b>) temperature (meadow; herbosa; grassland); (<b>b2</b>) temperature (coniferous forest; broad-leaved forest; coniferous and broad-leaved forest); (<b>b3</b>) temperature (alpine vegetation; cultural vegetation); (<b>b4</b>) temperature (bush-wood; bog; desert; others).</p>
Full article ">Figure 9
<p>Lag time in different regions: (<b>a1</b>) Northeast China (precipitation); (<b>a2</b>) Northeast China (temperature); (<b>b1</b>) East China (precipitation); (<b>b2</b>) East China (temperature); (<b>c1</b>) Northwest China (precipitation); (<b>c2</b>) Northwest China (temperature); (<b>d1</b>) North China (precipitation); (<b>d2</b>) North China (temperature); (<b>e1</b>) Southwest China (precipitation); (<b>e2</b>) Southwest China (temperature); (<b>f1</b>) Central–South China (precipitation); (<b>f2</b>) Central–South China (temperature).</p>
Full article ">Figure 9 Cont.
<p>Lag time in different regions: (<b>a1</b>) Northeast China (precipitation); (<b>a2</b>) Northeast China (temperature); (<b>b1</b>) East China (precipitation); (<b>b2</b>) East China (temperature); (<b>c1</b>) Northwest China (precipitation); (<b>c2</b>) Northwest China (temperature); (<b>d1</b>) North China (precipitation); (<b>d2</b>) North China (temperature); (<b>e1</b>) Southwest China (precipitation); (<b>e2</b>) Southwest China (temperature); (<b>f1</b>) Central–South China (precipitation); (<b>f2</b>) Central–South China (temperature).</p>
Full article ">Figure 10
<p>Partial correlation coefficient and significance test: (<b>a1</b>) temperature partial correlation coefficient; (<b>a2</b>) temperature significance test; (<b>b1</b>) precipitation partial correlation coefficient; (<b>b2</b>) precipitation significance test; (<b>c1</b>) accumulated temperature partial correlation coefficient; (<b>c2</b>) accumulated temperature significance test.</p>
Full article ">Figure 10 Cont.
<p>Partial correlation coefficient and significance test: (<b>a1</b>) temperature partial correlation coefficient; (<b>a2</b>) temperature significance test; (<b>b1</b>) precipitation partial correlation coefficient; (<b>b2</b>) precipitation significance test; (<b>c1</b>) accumulated temperature partial correlation coefficient; (<b>c2</b>) accumulated temperature significance test.</p>
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<p>The distribution map of NDVI dominant factors in China’s six major zones.</p>
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<p>Single factors: (<b>a</b>) Northeast China; (<b>b</b>) North China; (<b>c</b>) East China; (<b>d</b>) Northwest China; (<b>e</b>) Southwest China; (<b>f</b>) Central–South China.</p>
Full article ">Figure 12 Cont.
<p>Single factors: (<b>a</b>) Northeast China; (<b>b</b>) North China; (<b>c</b>) East China; (<b>d</b>) Northwest China; (<b>e</b>) Southwest China; (<b>f</b>) Central–South China.</p>
Full article ">Figure 13
<p>Interaction factors: (<b>a1</b>) Northeast China (2000); (<b>a2</b>) Northeast China (2010); (<b>a3</b>) Northeast China (2020); (<b>b1</b>) North China (2000); (<b>b2</b>) North China (2010); (<b>b3</b>) North China (2020); (<b>c1</b>) East China (2000); (<b>c2</b>) East China (2010); (<b>c3</b>) East China (2020); (<b>d1</b>) Northwest China (2000); (<b>d2</b>) Northwest China (2010); (<b>d3</b>) Northwest China (2020); (<b>e1</b>) Southwest China (2000); (<b>e2</b>) Southwest China (2010); (<b>e3</b>) Southwest China (2020); (<b>f1</b>) Central China (2000); (<b>f2</b>) Central China (2010); (<b>f3</b>) Central–South China (2020).</p>
Full article ">Figure 13 Cont.
<p>Interaction factors: (<b>a1</b>) Northeast China (2000); (<b>a2</b>) Northeast China (2010); (<b>a3</b>) Northeast China (2020); (<b>b1</b>) North China (2000); (<b>b2</b>) North China (2010); (<b>b3</b>) North China (2020); (<b>c1</b>) East China (2000); (<b>c2</b>) East China (2010); (<b>c3</b>) East China (2020); (<b>d1</b>) Northwest China (2000); (<b>d2</b>) Northwest China (2010); (<b>d3</b>) Northwest China (2020); (<b>e1</b>) Southwest China (2000); (<b>e2</b>) Southwest China (2010); (<b>e3</b>) Southwest China (2020); (<b>f1</b>) Central China (2000); (<b>f2</b>) Central China (2010); (<b>f3</b>) Central–South China (2020).</p>
Full article ">Figure 13 Cont.
<p>Interaction factors: (<b>a1</b>) Northeast China (2000); (<b>a2</b>) Northeast China (2010); (<b>a3</b>) Northeast China (2020); (<b>b1</b>) North China (2000); (<b>b2</b>) North China (2010); (<b>b3</b>) North China (2020); (<b>c1</b>) East China (2000); (<b>c2</b>) East China (2010); (<b>c3</b>) East China (2020); (<b>d1</b>) Northwest China (2000); (<b>d2</b>) Northwest China (2010); (<b>d3</b>) Northwest China (2020); (<b>e1</b>) Southwest China (2000); (<b>e2</b>) Southwest China (2010); (<b>e3</b>) Southwest China (2020); (<b>f1</b>) Central China (2000); (<b>f2</b>) Central China (2010); (<b>f3</b>) Central–South China (2020).</p>
Full article ">
16 pages, 2740 KiB  
Article
Brewing with Sea Vegetable: The Effect of Spirulina (Arthrospira platensis) Supplementation on Brewing Fermentation Kinetics, Yeast Behavior, and the Physiochemical Properties of the Product
by Alexa Pérez-Alva, Mario Guadalupe-Daqui, Santiago Cárdenas-Pinto, Skylar R. Moreno, Katherine A. Thompson-Witrick, Melissa A. Ramírez-Rodrigues, Milena M. Ramírez-Rodrigues and Andrew J. MacIntosh
Fermentation 2024, 10(8), 415; https://doi.org/10.3390/fermentation10080415 (registering DOI) - 11 Aug 2024
Abstract
Spirulina is a highly nutritious microalgae commonly used as a food additive. During fermentation, different adjuncts are incorporated to act as a nutrient source for yeast and fortify or modify the sensory attributes of the final product. In this study, the effect of [...] Read more.
Spirulina is a highly nutritious microalgae commonly used as a food additive. During fermentation, different adjuncts are incorporated to act as a nutrient source for yeast and fortify or modify the sensory attributes of the final product. In this study, the effect of Spirulina on the characteristics of controlled yeast fermentation and the production of volatile organic compounds (VOCs) was analyzed. Spirulina was added to malted barley during mashing and fermented under standard conditions. An unaltered mash (negative control) and yeast extract (positive control) were also fermented. The addition of Spirulina resulted in an increased fermentation rate (~14% faster) and bigger yeast cells (~34% larger) in comparison to the negative control. There were differences in color (determined as SRM) between treatments; however, there were only minor differences in VOCs, with no statistical differences observed between chemical compound groups. No differences were observed in the pH, total number of yeast cells, or final attenuation between treatments. The primary mechanism for the observed differences is believed to be an increase in amino acids available to yeast that were contributed by the Spirulina. This shows both that Spirulina has a high potential as a fermentation adjunct and that the amino acid profile of an adjunct can significantly impact fermentation. Full article
(This article belongs to the Special Issue Advances in Fermented Fruits and Vegetables)
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Figure 1

Figure 1
<p>(<b>a</b>) Fermentation density over time for each treatment: control (yellow), spirulina fortified (green), and yeast extract fortified (orange). Data points (circles, square, triangles) represent the average value of triplicate measurements, and models (continuous lines) were built using a four-parameter logistic model as described in the ASBC Yeast-14 method. (<b>b</b>) pH over time for each treatment: control (yellow), spirulina fortified (green), and yeast extract fortified (orange). Points (circles, square, triangles) represent the average value of triplicate measurements, and models (continuous lines) were built using a four-parameter logistic model as described in the ASBC Yeast-14 method. (<b>c</b>) Yeast cells in suspension during fermentation for each treatment: control (yellow), spirulina fortified (green), and yeast extract fortified (orange). Data points (circles, square, triangles) represent the average value of triplicate measurements, and models (continuous lines) were built using a tilted Gaussian fit model logistic model as described in the ASBC Yeast-14 method. (<b>d</b>) Rate of sugar metabolism during the fermentation determined by calculating the first derivative of the sugar consumption model (<a href="#fermentation-10-00415-f001" class="html-fig">Figure 1</a>a) as described by Guadalupe et al. [<a href="#B42-fermentation-10-00415" class="html-bibr">42</a>].</p>
Full article ">Figure 1 Cont.
<p>(<b>a</b>) Fermentation density over time for each treatment: control (yellow), spirulina fortified (green), and yeast extract fortified (orange). Data points (circles, square, triangles) represent the average value of triplicate measurements, and models (continuous lines) were built using a four-parameter logistic model as described in the ASBC Yeast-14 method. (<b>b</b>) pH over time for each treatment: control (yellow), spirulina fortified (green), and yeast extract fortified (orange). Points (circles, square, triangles) represent the average value of triplicate measurements, and models (continuous lines) were built using a four-parameter logistic model as described in the ASBC Yeast-14 method. (<b>c</b>) Yeast cells in suspension during fermentation for each treatment: control (yellow), spirulina fortified (green), and yeast extract fortified (orange). Data points (circles, square, triangles) represent the average value of triplicate measurements, and models (continuous lines) were built using a tilted Gaussian fit model logistic model as described in the ASBC Yeast-14 method. (<b>d</b>) Rate of sugar metabolism during the fermentation determined by calculating the first derivative of the sugar consumption model (<a href="#fermentation-10-00415-f001" class="html-fig">Figure 1</a>a) as described by Guadalupe et al. [<a href="#B42-fermentation-10-00415" class="html-bibr">42</a>].</p>
Full article ">
11 pages, 268 KiB  
Review
General Dietary Recommendations for People with Down Syndrome
by Joanna Gruszka and Dariusz Włodarek
Nutrients 2024, 16(16), 2656; https://doi.org/10.3390/nu16162656 (registering DOI) - 11 Aug 2024
Abstract
Down syndrome (DS) is caused by trisomy of chromosome 21 and is associated with characteristic features of appearance, intellectual impairment to varying degrees, organ defects, and health problems typical of this syndrome. Studies on the frequency of consumption of food products in this [...] Read more.
Down syndrome (DS) is caused by trisomy of chromosome 21 and is associated with characteristic features of appearance, intellectual impairment to varying degrees, organ defects, and health problems typical of this syndrome. Studies on the frequency of consumption of food products in this group show many irregularities, in particular too low consumption of vegetables and fruits, wholegrain cereal products and dairy products, and excessive consumption of meat products and sweets. It is necessary to correct eating habits. The diets of people with trisomy 21 should be consistent with the recommendations of rational nutrition for the general population and take into account specific dietary modifications related to the occurrence of diseases and health problems characteristic of this syndrome. Full article
(This article belongs to the Section Nutrition and Public Health)
13 pages, 1470 KiB  
Article
Reproductive Success of Tree Swallows at Abandoned Mine Drainage Treatment Ponds
by James S. Kellam, Julianna E. Lott, Anna R. Doelling and Isabella Ladisic
Birds 2024, 5(3), 440-452; https://doi.org/10.3390/birds5030030 (registering DOI) - 10 Aug 2024
Viewed by 229
Abstract
Abandoned mine drainage treatment ponds could have contrasting effects on the reproductive success of birds living in the vicinity. The ponds and associated vegetation may, like any other body of freshwater, provide beneficial habitats for the insects that the birds use to feed [...] Read more.
Abandoned mine drainage treatment ponds could have contrasting effects on the reproductive success of birds living in the vicinity. The ponds and associated vegetation may, like any other body of freshwater, provide beneficial habitats for the insects that the birds use to feed their young; or instead, the ponds may act as an ecological trap, attracting the birds to a habitat that is poor in quality and negatively impacting their productivity. We monitored nests of an aerial insectivore, the Tree Swallow (Tachycineta bicolor), to determine whether the distance between the ponds and the nests affected various reproductive parameters including clutch size, hatch rate, number of nestlings, nestling size and mass, number of fledglings, fledging rate, and fledge date. Data were collected over two breeding seasons (2022 and 2023) from a swallow population in southwestern Pennsylvania, USA. We found that the nests closest to the treatment ponds had significantly more nestlings and fledglings, earlier fledge dates, and a better fledging rate when compared to nests that were more distant from the ponds. However, all these parameters were well below previously published values, which suggests that the mine drainage ponds provide good nesting habitats relative to what is available in the region but that they do not represent high-quality habitats for this species overall. Full article
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Figure 1

Figure 1
<p>(<b>a</b>) Location of the study site within the United States. (<b>b</b>) Aerial view of the study site. Tree Swallow nest boxes were placed near abandoned mine drainage (AMD) treatment ponds and at locations that were more distant from the ponds. Nest box locations are shown on the map as red squares (NEAR locations), yellow squares (MID locations), and purple squares (FAR locations). Box locations are approximated.</p>
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<p>Average numbers ± SE of Tree Swallow eggs, hatched young, and fledglings at nests NEAR to (red points), at MID distance from (yellow points), and FAR from (purple points) abandoned mine drainage ponds. Overall means of three distance groups were significantly different.</p>
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<p>Wing lengths of nestling Tree Swallows and age at time of measurement. Only one nestling was measured per nest. The positive relationship between the two variables was significant (<span class="html-italic">p</span> &lt; 0.001), with no difference between slopes of nests in the NEAR (red points), MID (yellow points), and FAR (blue points) groups (<span class="html-italic">p</span> = 0.436). Regression lines and 95% confidence intervals are shown for each group. The R<sup>2</sup> values for the regression lines are as follows: NEAR = 0.45, MID = 0.55, FAR = 0.73).</p>
Full article ">Figure 4
<p>The mass of nestling Tree Swallows and age at time of measurement. Only one nestling was measured per nest. The positive relationship between the two variables was significant (<span class="html-italic">p</span> = 0.002), with no difference found between nests in the NEAR (red points), MID (yellow points), and FAR (blue points) groups (<span class="html-italic">p</span> = 0.451). Regression lines and 95% confidence intervals are shown for each group. The R<sup>2</sup> values for the regression lines are as follows: NEAR = 0.02, MID = 0.59, FAR = 0.59).</p>
Full article ">
15 pages, 4640 KiB  
Article
Study of the Preparation and Properties of Chemically Modified Materials Based on Rapeseed Meal
by Sara Aquilia, Luca Rosi, Michele Pinna, Sabrina Bianchi, Walter Giurlani, Marco Bonechi, Francesco Ciardelli, Anna Maria Papini and Claudia Bello
Biomolecules 2024, 14(8), 982; https://doi.org/10.3390/biom14080982 (registering DOI) - 10 Aug 2024
Viewed by 167
Abstract
In recent years, there has been increasing interest in developing novel materials based on natural biopolymers as a renewable alternative to petroleum-based plastics. The availability of proteins derived from agricultural by-products, along with their favourable properties, has fostered a renewed interest in protein-based [...] Read more.
In recent years, there has been increasing interest in developing novel materials based on natural biopolymers as a renewable alternative to petroleum-based plastics. The availability of proteins derived from agricultural by-products, along with their favourable properties, has fostered a renewed interest in protein-based materials, promoting research in innovative technologies. In this study, we propose the use of rapeseed protein-rich meal as the main ingredient for the preparation of novel sustainable materials combining excellent environmental properties such as biodegradability and renewability. The application of sustainable products in the present high-tech society requires the modification of the basic native properties of these natural compounds. The original route proposed in this paper consists of preparation via the compression moulding of flexible biomaterials stabilized by crosslinkers/chain extenders. An investigation of the effects of different denaturing and disulfide bond reducing agents, crosslinkers, and preparation conditions on the material mechanical behaviour demonstrated that the novel materials have appreciable strength and stiffness. The results show the potential of utilizing full meal from vegetable by-products to prepare protein-based materials with guaranteed ecofriendly characteristics and mechanical properties adequate for specific structural applications. Full article
(This article belongs to the Collection Feature Papers in 'Biological and Bio- Materials' Section)
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Figure 1
<p>Histogram of tensile properties (<b>left</b>) and stress/strain diagram (<b>right</b>) of meal/casein/water/glycerol blends without or with the addition of Na<sub>2</sub>SO<sub>3</sub> and/or a denaturing agent (for specimens’ composition refers to <a href="#biomolecules-14-00982-t002" class="html-table">Table 2</a>).</p>
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<p>First-derivative curve of TGA of rapeseed meal protein-based materials RM-R0, RM, RM-R2, RM-D1, RM-D2, and RM-R2D2 14%.</p>
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<p>Overlapped full FT-IR spectra of rapeseed-meal-based materials.</p>
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<p>FT-IR spectra of amide I band of: (<b>A</b>) RM-R0, RM, RM-R2, RM-D1, RM-D2, and RM-R2D2 14% materials. (<b>B</b>) Rapeseed meal, RM-R0, RM, and RM-R2D2 14% materials (baseline corrected).</p>
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<p>Scanning electron microscopy images of surface (<b>left</b>) and cryofracture cross-section (<b>right</b>) of specimens RM-R0, RM-D2, and RM-R2D2 14%.</p>
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28 pages, 2192 KiB  
Review
Integrated Nutrient Management of Fruits, Vegetables, and Crops through the Use of Biostimulants, Soilless Cultivation, and Traditional and Modern Approaches—A Mini Review
by Awais Ali, Genhua Niu, Joseph Masabni, Antonio Ferrante and Giacomo Cocetta
Agriculture 2024, 14(8), 1330; https://doi.org/10.3390/agriculture14081330 (registering DOI) - 9 Aug 2024
Viewed by 647
Abstract
The increasing population, its requirements for food, and the environmental impact of the excessive use of inputs make crop production a pressing challenge. Integrated nutrient management (INM) has emerged as a critical solution by maximizing nutrient availability and utilization for crops and vegetables. [...] Read more.
The increasing population, its requirements for food, and the environmental impact of the excessive use of inputs make crop production a pressing challenge. Integrated nutrient management (INM) has emerged as a critical solution by maximizing nutrient availability and utilization for crops and vegetables. This review paper highlights the potential benefits of INM for various vegetables and field crops and explores the conceptual strategies, components, and principles underlying this approach. Studies have shown that a wide range of vegetables and field crops benefit from INM, in terms of increased yield and improvements in yield attributes, nutrient contents and uptake, growth parameters, and various physiological and biochemical characteristics. This paper discusses biostimulants, their categories, and their impact on plant propagation, growth, photosynthesis, seed germination, fruit set, and quality. Additionally, this review explores modern sustainable soilless production techniques such as hydroponics, aeroponics, and aquaponics. These cultivation methods highlight the advancements of controlled-environment agriculture (CEA) and its contribution to nutrient management, food security and minimizing the environmental footprint. The review concludes by proposing methods and fostering discussions on INM’s future development, while acknowledging the challenges associated with its adoption. Finally, this review emphasizes the substantial evidence supporting INM as a novel and ecologically sound strategy for achieving sustainable agricultural production worldwide. Full article
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Figure 1
<p>Biostimulants and their different categories employed in agriculture.</p>
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<p>Components of INM.</p>
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<p>Flow sheet diagram highlighting the advantages offered by integrated nutrient management.</p>
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<p>Biostimulants and their applications and impacts on different stages of ornamentals, fruits, vegetables, and field crops.</p>
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<p>A sketch of soilless cultivation systems.</p>
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16 pages, 2316 KiB  
Article
Characteristics and Potential Use of Fruits from Different Varietal Groups of Sechium edule (Jacq.) Sw
by Edgar Adrián Rivera-Ponce, Ma. de Lourdes Arévalo-Galarza, Jorge Cadena-Iñiguez, Marcos Soto-Hernández, Yeimy Ramírez-Rodas and Cecilia García-Osorio
Horticulturae 2024, 10(8), 844; https://doi.org/10.3390/horticulturae10080844 - 9 Aug 2024
Viewed by 158
Abstract
(1) Background: Chayote [Sechium edule Jacq. (Sw.)] is a non-traditional export product; recently, demand has increased due to its nutritional and functional properties. There is a wide diversity of varietal groups (VGs) within this species. Despite this, only virens levis and nigrum [...] Read more.
(1) Background: Chayote [Sechium edule Jacq. (Sw.)] is a non-traditional export product; recently, demand has increased due to its nutritional and functional properties. There is a wide diversity of varietal groups (VGs) within this species. Despite this, only virens levis and nigrum spinosum varieties are commercialized on a large scale, while the rest are underutilized and poorly studied, so the genetic pool of this species is at risk. (2) Methods: The following variables were evaluated in the fruits of 10 chayote groups of varieties: shape, size, weight, stomatal frequency (SF), stoma size, stomatal index (SI), color index (CO*), pigments, titratable acidity (TA), total soluble solids (TSS), total sugars and moisture content. In addition, the postharvest behavior of the ten VGs stored at room temperature and the effect of 1-MCP on fruit quality during cold storage were evaluated. (3) Results: The groups a. minor and n. minor showed rapid weight loss, the albus varieties showed high epidermis oxidation, while v. levis, n. maximum, n. spinosum and n. xalapensis were susceptible to viviparity, blisters and fungal incidence. 1-MCP prevented chilling injury (CI) and weight loss. (4) Conclusions: The diversity of postharvest characteristics allows the use of VGs for different uses such as a fresh fruit, agroindustrial transformation or mixing with other vegetables. Full article
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<p>Morphological characteristics of 10 varietal groups of chayote [<span class="html-italic">Sechium edule</span> Jacq. (Sw.)]: (<b>1</b>) <span class="html-italic">a. minor</span>; (<b>2</b>) <span class="html-italic">a. levis</span>; (<b>3</b>) <span class="html-italic">a. dulcis</span>; (<b>4</b>) <span class="html-italic">a. spinosum</span>; (<b>5</b>) <span class="html-italic">a. levis gigante</span>; (<b>6</b>) <span class="html-italic">v. levis</span>; (<b>7</b>) <span class="html-italic">n. minor</span>; (<b>8</b>) <span class="html-italic">n. xalapensis</span>; (<b>9</b>) <span class="html-italic">n. spinosum</span>; (<b>10</b>) <span class="html-italic">n. maxima</span>. The letters (a, b, c, d, e, f) represent different morphotypes within the same varietal group. Bar size 3 cm.</p>
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<p>Cluster dendrogram of the physicochemical and morphological characteristics of 10 varietal groups of chayote [<span class="html-italic">Sechium edule</span> Jacq. (Sw.)]. Roman numerals I–IV indicate the groups similarity among chayote varieties.</p>
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<p>(<b>a</b>) Hierarchical map of postharvest disorders that initially affect the loss of quality of 10 varietal groups of chayote [<span class="html-italic">Sechium edule</span> Jacq. (Sw.)]; (<b>b</b>) Fruit chayote varieties with different symptoms and disorders that can affect their organoleptic quality.</p>
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<p>Percentage of germinated fruits of chayote [<span class="html-italic">Sechium edule</span> Jacq. (Sw.)] var. <span class="html-italic">v. levis</span>, <span class="html-italic">n. xalapensis</span>, <span class="html-italic">n. spinosum</span> and <span class="html-italic">n. maxima</span> at days 0, 3, 5, 7 and 14 in storage at room temperature (21 °C and 70% RH) (n = 15).</p>
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<p>Activity of α-amylase of chayote [<span class="html-italic">Sechium edule</span> Jacq. (Sw.)] var. <span class="html-italic">v</span>. <span class="html-italic">levis</span>, <span class="html-italic">n</span>. <span class="html-italic">xalapensis</span>, <span class="html-italic">n. spinosum and n. maxima</span> at days 0, 3, 5, and 7 in storage at room temperature (21 °C and 70% RH). Different letters on the same day indicate significant differences between means according to Kruskall–Wallis (α = 0.05) (n = 6 ± SE).</p>
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<p>Effect of 1-MCP on the commercial quality of chayote fruits after refrigerated storage (8.7 °C, 95% RH). Letters show the treatments: (<b>a</b>) control and (<b>b</b>) 1-MCP treatment. The cold storage for 2 weeks are (<b>A</b>) <span class="html-italic">a</span>. <span class="html-italic">minor</span> and (<b>B</b>) <span class="html-italic">n. minor</span>. The storage for 3 weeks are (<b>C</b>) <span class="html-italic">a. dulcis</span>, (<b>D</b>) <span class="html-italic">v. levis</span> (<b>A</b>,<b>E</b>) <span class="html-italic">a. levis</span>.</p>
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13 pages, 3497 KiB  
Technical Note
Analysis of Changes in Forest Vegetation Peak Growth Metrics and Driving Factors in a Typical Climatic Transition Zone: A Case Study of the Funiu Mountain, China
by Jiao Tang, Huimin Wang, Nan Cong, Jiaxing Zu and Yuanzheng Yang
Remote Sens. 2024, 16(16), 2921; https://doi.org/10.3390/rs16162921 - 9 Aug 2024
Viewed by 212
Abstract
Phenology and photosynthetic capacity both regulate carbon uptake by vegetation. Previous research investigating the impact of phenology on vegetation productivity has focused predominantly on the start and end of growing seasons (SOS and EOS), leaving the influence of peak phenology metrics—particularly in typical [...] Read more.
Phenology and photosynthetic capacity both regulate carbon uptake by vegetation. Previous research investigating the impact of phenology on vegetation productivity has focused predominantly on the start and end of growing seasons (SOS and EOS), leaving the influence of peak phenology metrics—particularly in typical climatic transition zones—relatively unexplored. Using a 24-year (2000–2023) enhanced vegetation index (EVI) dataset from the Moderate Resolution Imaging Spectroradiometer (MODIS), we extracted and examined the spatiotemporal variation for peak of season (POS) and peak growth (defined as EVImax) of forest vegetation in the Funiu Mountain region, China. In addition to quantifying the factors influencing the peak phenology metrics, the relationship between vegetation productivity and peak phenological metrics (POS and EVImax) was investigated. Our findings reveal that POS and EVImax showed advancement and increase, respectively, negatively and positively correlated with vegetation productivity. This suggested that variations in EVImax and peak phenology both increase vegetation productivity. Our analysis also showed that EVImax was heavily impacted by precipitation, whereas SOS had the greatest effect on POS variation. Our findings highlighted the significance of considering climate variables as well as biological rhythms when examining the global carbon cycle and phenological shifts in response to climate change. Full article
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<p>The (<b>a</b>) elevation and (<b>b</b>) forest vegetation distribution in the Funiu Mountain region.</p>
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<p>Phenological extraction diagram in this research. Notes: The X-axis represents the Day of Year (DOY).</p>
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<p>Interannual trends of (<b>a</b>) POS, (<b>b</b>) EVI<sub>max</sub> and (<b>c</b>) their relationships in the Funiu Mountain region.</p>
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<p>Spatial distribution of average (<b>a</b>) POS, (<b>b</b>) EVI<sub>max</sub>, (<b>c</b>) trends of POS and (<b>d</b>) trends of EVI<sub>max</sub> in the Funiu Mountain region from 2000 to 2023. Notes: The Y-axis of the histogram insert represents the frequence of pixels count in this figure and the next.</p>
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<p>Spatial patterns of partial correlations between vegetation productivity and (<b>a</b>) POS and (<b>b</b>) EVI<sub>max</sub>. Notes: The NC and PC indicate negative correlation and positive correlation respectively in the legend in this figure and in the figure below.</p>
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<p>Spatial patterns of partial correlation between POS and SOS, preseason temperature, precipitation are given in (<b>a</b>–<b>c</b>), while the spatial patterns between above variables and EVI<sub>max</sub> are given in (<b>d</b>–<b>f</b>).</p>
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<p>The most significant factor controlling (<b>a</b>) POS and (<b>b</b>) EVI<sub>max,</sub> according to the significance level of the partial correlation.</p>
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<p>Mean EVI curve during 2000–2007, 2008–2015, 2016–2023 and 2000–2023 averaged across the forest area in the Funiu Mountain.</p>
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11 pages, 1542 KiB  
Article
Long-Term Monitoring of European Brown Hare (Lepus europaeus) Population in the Slovak Danubian Lowland
by Francesco Vizzarri, Jaroslav Slamecka, Tomas Sladecek, Rastislav Jurcik, Lubomir Ondruska and Peter Schultz
Diversity 2024, 16(8), 486; https://doi.org/10.3390/d16080486 - 9 Aug 2024
Viewed by 168
Abstract
In many European countries over the last few decades, arable fields dominate agricultural landscapes, leading to very intensive land-use practices. This seems to be the main cause of population declines for numerous farmland species, including the European brown hare (Lepus europaeus Pallas, [...] Read more.
In many European countries over the last few decades, arable fields dominate agricultural landscapes, leading to very intensive land-use practices. This seems to be the main cause of population declines for numerous farmland species, including the European brown hare (Lepus europaeus Pallas, 1778). The Research Institute for Animal Production (National Agricultural and Food Centre—NPPC, Luzianky, Slovakia) has been engaged in a long monitoring project (a project currently running), collecting certain indicators of brown hare population dynamics during hunting season from 1987 to 2023 in the Slovak Danubian Lowland. In the same macro-area (Čiližská Radvaň and Lehnice), a study was conducted on the influence of permanent semi-natural vegetation in relation to recruitment, population density and production. The entire monitored period was aggregated into 5-year intervals (for a total of seven time intervals), with the aim of analyzing the brown hare population dynamics. Spring hare density in the Danubian Lowland is currently 20.8 hares/km2, with harvests of 4.6 hares/km2. During the monitoring period, bag animals have been examined following the regular hunting operations for the purpose of age determination (weight of eye lenses), sex ratio and productivity. There was a large positive effect of set-aside with special mixtures created for hares in large-scale farmed agrarian landscapes on brown hare density, bag and recruitment. In-model hunting grounds with such set-asides increased the spring stock by 25%, bag by 100% and recruitment by 20%. This study reveals that the management of European brown hare is not sustainable in the Slovak Danubian Lowland, and the population is decreasing. This is proven through the decline in harvest brown hares and by population dynamic parameters. Our data suggest that improvements in the habitat quality of arable landscapes by the adoption of permanent semi-natural vegetation may be more effective in the increase in the brown hare population. Full article
(This article belongs to the Topic Land-Use Change, Rural Practices and Animal Diversity)
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<p>Geographical location of the Slovak Danubian Lowland (<b>A</b>) and the two model areas, Čiližská Radvaň (<b>B</b>) and Lehnice (<b>C</b>), for semi-natural habitats. Source: National Agricultural and Food Centre archive.</p>
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<p>Average data on precipitation (mm) and temperature (°C) in Slovak Danubian Lowland.</p>
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<p>Development of hare shooting and acreage of tilled set-aside (Čiližská Radvaň).</p>
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<p>Recruitment of brown hares (PYB = ratio of juveniles in the bag) and acreage of set-asides (Lehnice).</p>
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15 pages, 4739 KiB  
Article
Impacts of Fire Frequency on Net CO2 Emissions in the Cerrado Savanna Vegetation
by Letícia Gomes, Jéssica Schüler, Camila Silva, Ane Alencar, Bárbara Zimbres, Vera Arruda, Wallace Vieira da Silva, Edriano Souza, Julia Shimbo, Beatriz Schwantes Marimon, Eddie Lenza, Christopher William Fagg, Sabrina Miranda, Paulo Sérgio Morandi, Ben Hur Marimon-Junior and Mercedes Bustamante
Fire 2024, 7(8), 280; https://doi.org/10.3390/fire7080280 - 9 Aug 2024
Viewed by 454
Abstract
Savannas play a key role in estimating emissions. Climate change has impacted the Cerrado savanna carbon balance. We used the burned area product and long-term field inventories on post-fire vegetation regrowth to estimate the impact of the fire on greenhouse gas emissions and [...] Read more.
Savannas play a key role in estimating emissions. Climate change has impacted the Cerrado savanna carbon balance. We used the burned area product and long-term field inventories on post-fire vegetation regrowth to estimate the impact of the fire on greenhouse gas emissions and net carbon dioxide (CO2) emissions in the Cerrado savanna between 1985 and 2020. We estimated the immediate emissions from fires, CO2 emissions by plant mortality, and CO2 removal from vegetation regrowth. The burned area was 29,433 km2; savanna fires emitted approximately 2,227,964 Gg of CO2, 85,057 Gg of CO, 3010 Gg of CH4, 5,103 Gg of NOx, and 275 Gg of N2O. We simulated vegetation regrowth according to three fire regime scenarios: extreme (high fire frequency and short fire interval), intermediate (medium fire frequency and medium fire interval), and moderate (low fire frequency and long fire interval). Under the extreme and intermediate scenarios, the vegetation biomass decreased by 2.0 and 0.4% (ton/ha-year), while the biomass increased by 2.1% under a moderate scenario. We converted this biomass into CO2 and showed that the vegetation regrowth removed 63.5% of the total CO2 emitted (2,355,426 Gg), indicating that the Cerrado savanna has been a source of CO2 to the atmosphere. Full article
(This article belongs to the Section Fire Science Models, Remote Sensing, and Data)
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<p>Integration of datasets for estimating the CO<sub>2</sub> emissions from fires not associated with deforestation in the Cerrado savanna. AGB = Aboveground biomass; FRI = Fire return interval index; Rectangle = Typifies the database; Parallelogram forms = Typifies the analysis.</p>
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<p>Relationships between fire frequency scenarios (frequency and interval) and aboveground biomass recovery in the Cerrado savanna. FF = Fire frequency; FI = Fire interval; Percentage values = Rate of annual increase in biomass; Circles= Superior limit; and Squared dots= Inferior limit.</p>
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<p>Steps to build the fire return interval index and biomass change for the Cerrado savanna. AGB = Aboveground biomass; FRI = Fire return interval index.</p>
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<p>Annual estimates of immediate CO<sub>2</sub> emissions (<b>a</b>) associated with the burned area (<b>b</b>), rainfall (<b>c</b>), and SST anomaly (<b>d</b>) between 1985 and 2020 in the Cerrado savanna. The dashed line indicates the correspondence between the emission peaks and climatic variables.</p>
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<p>Spatial and temporal distribution of fire frequency (<b>a</b>,<b>b</b>), years since last fire (<b>c</b>,<b>d</b>), fire return interval index (FRI) classified by recovery rate scenarios (<b>e</b>,<b>f</b>), and biomass change (<b>g</b>,<b>h</b>) in Cerrado savanna.</p>
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<p>Spatial distribution of land cover and land use classes (<b>a</b>) and accumulated fire in 2020 (<b>b</b>) in Cerrado savanna.</p>
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<p>Temporal distribution of the total emissions (by fire combustion and vegetation mortality), removals (by vegetation regrowth), and net emissions (total emissions and removals) of CO<sub>2</sub> between 1985 and 2020 in Cerrado savanna.</p>
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21 pages, 1254 KiB  
Review
The Brown Marmorated Stink Bug (Hemiptera: Pentatomidae)—A Major Challenge for Global Plant Production
by Martina Pajač Beus, Darija Lemić, Sandra Skendžić, Dana Čirjak and Ivana Pajač Živković
Agriculture 2024, 14(8), 1322; https://doi.org/10.3390/agriculture14081322 - 9 Aug 2024
Viewed by 371
Abstract
The brown marmorated stink bug Halyomorpha halys (Stål, 1855), native to East Asia, is an extremely polyphagous pest that infests more than 300 plant species from 49 families. In Europe and North America, this pest causes enormous damage to the production of economically [...] Read more.
The brown marmorated stink bug Halyomorpha halys (Stål, 1855), native to East Asia, is an extremely polyphagous pest that infests more than 300 plant species from 49 families. In Europe and North America, this pest causes enormous damage to the production of economically important crops (tree fruit, vegetables, field crops, and ornamental plants). Global warming favours its spread, as the rise in temperature results in the appearance of further generations of the pest. Halyomorpha halys (nymph and adult) causes damage typical of the Pentatomidae family by attacking host plants throughout their development (buds, stems, fruits, and pods). Ripe fruits are often disfigured, and later suberification and necrotic spots form on the fruit surface, making them accessible to plant pathogens that cause fruit rot and rendering them unmarketable. The increasing global importance of the pest suggests that more coordinated measures are needed to contain its spread. Understanding the biology and ecology of this species is crucial for the development of reliable monitoring and management strategies. Most insecticides available for the control of H. halys have a broad spectrum of modes of action and are not compatible with most integrated pest management systems, so biological control by natural enemies has recently been emphasised. Preventing excessive population growth requires early identification and effective control measures that can be developed quickly and applied rapidly while respecting the environment. This paper presents a comprehensive review of the latest findings on the global distribution of this important pest, its potential spread, biology and ecology, key host plants of economic importance, monitoring methods, and effective biological control strategies, as well as future perspectives for sustainable H. halys control measures. Full article
(This article belongs to the Special Issue Integrated Pest Management Systems in Agriculture)
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<p>Global distribution of <span class="html-italic">Halyomorpha halys</span>.</p>
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<p>The life cycle of <span class="html-italic">Halyomorpha halys</span> in Europe.</p>
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25 pages, 25356 KiB  
Article
Exploring the Relationship between Urbanization and Vegetation Ecological Quality Changes in the Guangdong–Hong Kong–Macao Greater Bay Area
by Yanyan Wu, Zhaohui Luo and Zhifeng Wu
Land 2024, 13(8), 1246; https://doi.org/10.3390/land13081246 - 8 Aug 2024
Viewed by 315
Abstract
Rapid global urbanization and its progress have profoundly affected urban vegetation. The ecological quality of urban vegetation is a vital indicator of regional ecological stability and health. A comprehensive assessment of the coupling coordination and coercive relationship between urbanization and the vegetation ecological [...] Read more.
Rapid global urbanization and its progress have profoundly affected urban vegetation. The ecological quality of urban vegetation is a vital indicator of regional ecological stability and health. A comprehensive assessment of the coupling coordination and coercive relationship between urbanization and the vegetation ecological quality is essential for promoting sustainable regional green development. Using the rapidly urbanizing Guangdong–Hong Kong–Macao Greater Bay Area (GBA) urban agglomeration in China as an example, this study evaluates the vegetation quality condition and the level of urbanization and explores the dynamic relationship between vegetation ecological quality and urbanization processes. This study introduces the vegetation ecological quality index (VEQI) based on net primary productivity (NPP) and fractional vegetation cover (FVC), as well as the comprehensive urbanization index (CUI) derived from gross domestic production (GDP), population density, and nighttime lighting data. The coupling coordination and Tapio decoupling models are employed to assess the degree of coupling coordination and the decoupling relationship between the VEQI and CUI across different periods. The results showed that (1) from 2000 to 2020, the VEQI in the GBA showed a significant increase, accompanied by continuous urbanization, particularly evident with the high CUI values in central areas; (2) the coupling coordination degree (CCD) exhibits high values and significant change slopes in the central GBA, indicating dynamic interactions between urbanization and vegetation ecological quality; (3) the decoupling states between the VEQI and CUI are dominated by weak decoupling (WD), strong decoupling (SD), expansive negative decoupling (END), and expansive coupling (EC), suggesting improvements in the relationship between urbanization and vegetation ecological quality; (4) the coordinated development level of the VEQI and CUI in the study area shows improvement, and their decoupling relationship displays a positive trend. Nevertheless, it remains crucial to address the impact of urbanization pressure on vegetation ecological quality and to implement proactive measures in response. The results of this study provide theoretical support for mesoscale development planning, monitoring vegetation ecological conditions, and formulating environmental policies. Full article
(This article belongs to the Special Issue Climate Mitigation Potential of Urban Ecological Restoration)
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<p>Geographical location and the elevation of the GBA.</p>
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<p>Decoupling states of the Tapio decoupling indicator. SD, strong decoupling; WD, weak decoupling; RD, recessive decoupling; EC, expansive coupling; RC, recessive coupling; END, expansive negative decoupling; WND, weak negative decoupling; SND, strong negative decoupling. The orange line in the figure represents a DI value of 1.2, whereas the blue line indicates a DI value of 0.8; the wheat block represents decoupling state, the aquamarine block represents coupling state, and the pink block represents negative decoupling state.</p>
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<p>Levels of vegetation ecological quality index (VEQI) in 2000, 2005, 2010, 2015, and 2020 and the average value for the period from 2000 to 2020.</p>
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<p>Proportions of different levels of the VEQI (proportions less than 1% are not labeled in the figure).</p>
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<p>The VEQI change trend from 2000 to 2020 (<b>a</b>) and slope significance levels (<b>b</b>) in the GBA. DS, decreased significantly (0.01 &lt; <span class="html-italic">p</span> ≤ 0.05); IS, increased significantly (0.01 &lt; <span class="html-italic">p</span> ≤ 0.05); DVS, decreased very significantly (<span class="html-italic">p</span> ≤ 0.01); IVS, increased very significantly (<span class="html-italic">p</span> ≤ 0.01); D_NS, decreased non-significantly (<span class="html-italic">p</span> &gt; 0.05); I_NS, increased non-significantly (<span class="html-italic">p</span> &gt; 0.05).</p>
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<p>Spatial comprehensive urbanization index (CUI) patterns on the township scale in the GBA in 2000, 2005, 2010, 2015, and 2020, along with the average value for 2000–2020.</p>
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<p>The CUI change trends (<b>a</b>) and slope significance levels (<b>b</b>) from 2000 to 2020 on the township scale in the GBA. DS, decreased significantly (0.01 &lt; <span class="html-italic">p</span> ≤ 0.05); IS, increased significantly (0.01 &lt; <span class="html-italic">p</span> ≤ 0.05); DVS, decreased very significantly (<span class="html-italic">p</span> ≤ 0.01); IVS, increased very significantly (<span class="html-italic">p</span> ≤ 0.01); D_NS, decreased non-significantly (<span class="html-italic">p</span> &gt; 0.05); I_NS, increased non-significantly (<span class="html-italic">p</span> &gt; 0.05).</p>
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<p>The coupling coordination degree (CCD) and its proportion changes in the GBA from 2000 to 2020.</p>
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<p>Trends (<b>a</b>) and slope significance tests (<b>b</b>) of the CCD from 2000 to 2020 on the township scale in the GBA. IS, increased significantly (0.01 &lt; <span class="html-italic">p</span> ≤ 0.05); IVS, increased very significantly (<span class="html-italic">p</span> ≤ 0.01); D_NS, decreased non-significantly (<span class="html-italic">p</span> &gt; 0.05); I_NS, increased non-significantly (<span class="html-italic">p</span> &gt; 0.05).</p>
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<p>Changes in the decoupling level between the VEQI and CUI at the township scale of the GBA in the different periods. DI: decoupling index; ∆VEQI: change rate of the vegetation ecological quality index; ∆CUI: change rate of comprehensive urbanization index. The meaning of the color of the lines in the figure is the same as in <a href="#land-13-01246-f002" class="html-fig">Figure 2</a>.</p>
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<p>Spatial distribution and proportion statistics of eight decoupling states at the township scale of the GBA in different periods (proportions less than 1% are not labeled in the figure). SD, strong decoupling; WD, weak decoupling; RD, recessive decoupling; EC, expansive coupling, RC, recessive coupling; END, expansive negative decoupling; WND, weak negative decoupling; SND, strong negative decoupling.</p>
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<p>Area conversion for different VEQI levels between 2000 and 2020 in the GBA.</p>
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11 pages, 2138 KiB  
Article
Selection of Tomato (Solanum lycopersicum) Hybrids Resistant to Fol, TYLCV, and TSWV with Early Maturity and Good Fruit Quality
by Richecarde Lafrance, Claudia Villicaña, José Benigno Valdéz-Torres, Raymundo Saúl García-Estrada, Manuel Alonzo Báez Sañudo, Mayra Janeth Esparza-Araiza and Josefina León-Félix
Horticulturae 2024, 10(8), 839; https://doi.org/10.3390/horticulturae10080839 - 8 Aug 2024
Viewed by 329
Abstract
Tomato (Solanum lycopersicum L.) is widely grown in the tropics, where its production is subjected to heavy disease losses. A goal of tomato breeders is genetic improvement of early maturity genotypes with higher fruit quality under challenging environmental conditions, such as the [...] Read more.
Tomato (Solanum lycopersicum L.) is widely grown in the tropics, where its production is subjected to heavy disease losses. A goal of tomato breeders is genetic improvement of early maturity genotypes with higher fruit quality under challenging environmental conditions, such as the presence of multiple pathogens, is the goal of tomato breeders. In Mexico, tomato is one of the main exported vegetables, grown in most of the northwestern states of the country, with the state of Sinaloa as one of the largest producers. In this study, we evaluated fruit quality parameters in 16 tomato hybrids (14 hybrids under development in Sinaloa and 2 as commercial lines), which were previously analyzed with molecular markers to detect gene resistance. The hybrids were harvested at the “red ripe” stage at three different harvest dates. Total soluble solids (TSS), titratable acidity, pH, color, firmness, and the TSS/acidity ratio were evaluated. Of the 16 hybrids analyzed, 2 showed the presence of genes for resistance to TYLCV, 7 for resistance to TSWV and Fol race 3, 15 for resistance to Fol race 2, and all 16 for resistance to Fol race l. Results show that most of the tomato hybrids analyzed during the three harvest dates met market standards reported in the USDA’s fresh tomato import regulations and Mexico Supreme Quality 2005 (MCS Mexico Calidad Suprema for its acronym in Spanish). However, two of the advanced developmental hybrids better met the market requirements and are also maturing early. Full article
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<p>Polar plot of the external and internal colors (<span class="html-italic">h</span>° &amp; chroma) for the three harvest dates. (<b>A</b>) An external color of the first harvest date; (<b>B</b>) the external color of the second harvest date; (<b>C</b>) the external color of the third harvest date; (<b>D</b>) the internal color of the first harvest date; (<b>E</b>) the internal color of the second harvest date; (<b>F</b>) the internal color of the third harvest date.</p>
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<p>Bar plots of the external and internal chromaticity and hue angle for the three harvest dates. (<b>A</b>) External chromaticity; (<b>B</b>) external hue angle; (<b>C</b>) internal chromaticity; (<b>D</b>) internal hue angle (chroma A, external chromaticity of the harvest date; chroma B, external chromaticity of the second harvest date; chroma C, external chromaticity of the third harvest date; chroma D, internal chromaticity of harvest date; chroma E, internal chromaticity of the second harvest date; chroma F, internal chromaticity of the third harvest date) (hue (<span class="html-italic">h</span>°) <span class="html-italic">a*</span>, external angle hue of the first harvest date; hue (<span class="html-italic">h</span>°) <span class="html-italic">b*</span>, external angle hue of the second harvest date; hue (<span class="html-italic">h</span>°) <span class="html-italic">C*</span>, external angle hue of the third harvest date; hue (<span class="html-italic">h</span>°) *d, internal angle hue of the first harvest date; hue (<span class="html-italic">h</span>°) <span class="html-italic">*e</span>, internal angle hue of the second harvest date; hue (<span class="html-italic">h</span>°) <span class="html-italic">*f</span>, internal angle hue of the third harvest date).</p>
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<p>Bar plots of the three harvest dates. (<b>A</b>) Total soluble solid; (<b>B</b>) firmness; (<b>C</b>), pH; (<b>D</b>) acidity; (<b>E</b>) ratio TSS/acidity (A, first harvest date; B, second harvest date; C, third harvest date).</p>
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14 pages, 4035 KiB  
Article
Transcriptomic Analysis of Alfalfa Flowering and the Dual Roles of MsAP1 in Floral Organ Identity and Flowering Time
by Xu Jiang, Huiting Cui, Zhen Wang, Ruicai Long, Qingchuan Yang and Junmei Kang
Agronomy 2024, 14(8), 1741; https://doi.org/10.3390/agronomy14081741 - 8 Aug 2024
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Abstract
Flowering, the transition from the vegetative to the reproductive stage, is vital for reproductive success, affecting forage quality, the yield of aboveground biomass, and seed production in alfalfa. To explore the transcriptomic profile of alfalfa flowering transition, we compared gene expression between shoot [...] Read more.
Flowering, the transition from the vegetative to the reproductive stage, is vital for reproductive success, affecting forage quality, the yield of aboveground biomass, and seed production in alfalfa. To explore the transcriptomic profile of alfalfa flowering transition, we compared gene expression between shoot apices (SAs) at the vegetative stage and flower buds (FBs) at the reproductive stage by mRNA sequencing. A total of 3,409 DEGs were identified, and based on gene ontology (GO), 42.53% of the most enriched 15 processes were associated with plant reproduction, including growth phase transition and floral organ development. For the former category, 79.1% of DEGs showed higher expression levels in SA than FB, suggesting they were sequentially turned on and off at the two test stages. For the DEGs encoding the components of circadian rhythm, sugar metabolism, phytohormone signaling, and floral organ identity genes, 60.71% showed higher abundance in FB than SA. Among them, MsAP1, an APETALA1 (AP1) homolog of Arabidopsis thaliana, showed high expression in flower buds and co-expressed with genes related to flower organ development. Moreover, ectopic expression of MsAP1 in Arabidopsis resulted in dwarfism and early flowering under long-day conditions. The MsAP1-overexpression plant displayed morphological abnormalities including fused whorls, enlarged pistils, determinate inflorescence, and small pods. In addition, MsAP1 is localized in the nucleus and exhibits significant transcriptional activity. These findings revealed a transcriptional regulation network of alfalfa transition from juvenile phase to flowering and provided genetic evidence of the dual role of MsAP1 in flowering and floral organ development. Full article
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<p>Transcriptome analysis of alfalfa shoot apical tissues on day 25 and the floral buds on day 35. (<b>a</b>) The kinetic growth analysis of alfalfa in terms of plant height under the normal conditions. (<b>b</b>,<b>c</b>) Image of alfalfa shoot apex (SA) and floral bud (FB) on day 25 and day 35, respectively. The tissues were used for mRNA sequencing. (<b>d</b>) Heatmap of differential gene expression profiles of SA (day 25) and FB (day 35). The heatmap was constructed using FPKM values and normalized to a range of zero to one. Red represents high FPKM values, and blue for low values. (<b>e</b>) DEGs identified in this study with cut off |log2Foldchange| &gt; 1. Red stands for the upregulated genes in FB relative to SA, and blue for the downregulated genes. (<b>f</b>) Linear regression analysis between mRNA sequencing data and the expression level test by RT-qPCR of the 15 randomly selected DEGs.</p>
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<p>Classification of the DEGs enriched in terms of biological process and pathway. (<b>a</b>) Analysis of biological processes of the DEGs. GO terms for growth stage transition are marked with dots, while GO terms for flower organ development are indicated with asterisks. (<b>b</b>) Top 15 enriched pathways via Mapman. (<b>c</b>) The most enriched GO function of the putative transcription factors. (<b>d</b>) Transcript profile of the DEGs involved in phytohormone IAA, GA, and CTK signaling. The scale represents normalized FPKM for the annotated genes via sequence homology. The gradient colors from red to blue denote high and low expression, respectively.</p>
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<p>Analysis of floral formation-related TFs. (<b>a</b>) Gene expression profiles of DEGs related to the timing of meristematic phase transition. The color scale indicates normalized FPKM changes in gene expression levels in alfalfa shoot apical and flower bud tissue. The gradient colors from red to blue indicate the abundance of gene expression from high to low. The arrow symbol represents the activation relationship. LFY, LEAFY; FUL, FRUITFULL; WUS, WUSCHEL; SOC1, SUPPRESSOR OF CONSTANS OVEREXPRESSION 1; SPL, squamosa promoter-binding-like protein. (<b>b</b>) Network analysis of <span class="html-italic">AP1</span> and <span class="html-italic">AP2</span> and their network genes. Pale purple lines indicate co-expression network and pale red lines indicate physical interaction in Arabidopsis. The red arrow symbol represents upregulated expression of genes in flower buds, while the blue symbol represents downregulated expression. (<b>c</b>) Analysis of <span class="html-italic">MsAP1</span> expression pattern in different organs in vegetative and reproductive growth phase. Tissue sampling during the vegetative growth stage was performed on the 25th day after harvesting, floral meristem tissues were collected on the 30th day, and stems, leaves, flower buds, and flowers during the flowering stage were collected on day 40. Data represent mean values (with error bars indicating standard deviations from 3 biological replicates), and different letters denote significance levels &lt; 0.01, determined by statistical analysis using one-way ANOVA.</p>
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<p>Transcriptional activity assay and subcellular localization investigation of MsAP1. (<b>a</b>) The subcellular localization of the MsAP1-GFP fusion protein transiently expressed in tobacco leaves. Images were captured using a confocal microscope. Label the two constructs (control upper and recombinant vector lower panel, respectively); scale bars: 100 µm. Green represents GFP fluorescence signal, and blue dots represent cell nuclei labeled with DAPI (4’,6-diamidino-2-phenylindole). (<b>b</b>) Schematic diagram of His reporter gene expression activated by MsAP1 in a yeast cell. GAL4-BD represents the binding domain of GAL4. (<b>c</b>) Assay of the transcriptional activation of MsAP1 in yeast (Y2H) cells. Yeast were transfected with pGBKT7-MsAP1 (BD-MsAP1), pGBKT7-GAL4AD (positive control), and pGBKT7 (BD, negative control), respectively. The transformed cells were streaked on SC/-T and selective medium (SC/-T-H + 15 mM 3-AT) to assess growth. SC/-T: synthetic dropout (SC) yeast growth medium lacking tryptophan, SC/-L-H: SC medium lacking tryptophan and histidine, and supply with 15 mM 3-AT.</p>
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<p>Overexpression of <span class="html-italic">MsAP1</span> promoted flowering and altered floral organ morphology in Arabidopsis. (<b>a</b>) The relative transcription level of <span class="html-italic">MsAP1</span>. (<b>b</b>) Image of the homozygous T2 seedlings on day 20 after germination (DAG) under the long-day conditions. WT: Col-0, OE1, and OE3 represented the two independent transgenic Arabidopsis lines (<span class="html-italic">35S::MsAP1-GFP</span>). Bar = 2 cm. (<b>c</b>) Flowering time analysis in terms of days to bolting under the long-day conditions. (<b>d</b>) Analysis of rosette leaf number at the emergence of the first flower under the long-day conditions. (<b>e</b>) Phenotypes of the <span class="html-italic">MsAP1</span> overexpressing Arabidopsis terminal flowers, bar = 3 mm. (<b>f</b>) Phenotype of the fruit of Arabidopsis <span class="html-italic">thaliana</span>. Bar = 5 mm. (<b>g</b>) Relative transcription levels of the key genes related to Arabidopsis floral transition. Asterisks indicate significant difference at <span class="html-italic">p</span> &lt; 0.01 compared with wild type by Student’s <span class="html-italic">t</span>-test.</p>
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Article
Effects of Biogas Digestate on Winter Wheat Yield, Nitrogen Balance, and Nitrous Oxide Emissions under Organic Farming Conditions
by Felizitas Winkhart, Harald Schmid and Kurt-Jürgen Hülsbergen
Agronomy 2024, 14(8), 1739; https://doi.org/10.3390/agronomy14081739 - 8 Aug 2024
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Abstract
Biogas digestate is increasingly used in organic farming to improve soil nutrient supply and sustainably increase yields. However, biogas digestate can also lead to environmentally relevant N2O emissions. The benefits, opportunities, and risks associated with the use of digestate as a [...] Read more.
Biogas digestate is increasingly used in organic farming to improve soil nutrient supply and sustainably increase yields. However, biogas digestate can also lead to environmentally relevant N2O emissions. The benefits, opportunities, and risks associated with the use of digestate as a fertilizer in organic farming are a subject of ongoing debate, in part due to a lack of conclusive experimental results. A field trial conducted in southern Germany examined the short-term and long-term impacts of digestate fertilization on winter wheat yield, nitrogen use efficiency, and N2O-N emissions. The four-year results from the years 2019 to 2022 are presented. Digestate was applied with a nitrogen input of up to 265 kg ha−1, with 129 kg ha−1 NH4+-N. The application of digestate resulted in a significant increase in wheat yield, with an average increase of 53% (2019) to 83% (2022) compared to the unfertilized control. It is notable that the treatment applied for the first time did not reach the yield of the long-term fertilized treatment, with a yield gap of 0.5 to 1.2 Mg ha−1 (6% to 15%). The highest N2O-N emissions (up to 3.30 kg ha−1) in the vegetation period from spring to autumn were measured in the long-term fertilized treatment. However, very high N2O-N emissions (up to 3.72 kg ha−1) also occurred in two years in winter in the unfertilized treatment. An increase in soil inorganic N stocks and N2O-N emissions was observed following the wheat harvest and subsequent tillage in all treatments. No significant differences were identified between the fertilizer treatments with regard to product-related emissions. The experimental results demonstrate that N2O-N emissions are not solely a consequence of N fertilization, but can also be attributed to tillage, post-harvest practices, and previous crops, with considerable variability depending on weather conditions. The experimental data provide comprehensive insight into the influence of cultivation, soil characteristics, and meteorological conditions on N2O-N emissions at an agricultural site in southern Germany. Full article
(This article belongs to the Section Innovative Cropping Systems)
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<p>Ammonium-N (<b>a</b>) and nitrate-N dynamics (<b>b</b>) and soil moisture (<b>c</b>) of all four treatments related to the topsoil layer (0–15 cm) from March 2019 to September 2019. Nitrous oxide emissions (<b>d</b>), error bars illustrate the standard deviation) and temperature (red line) and precipitation (<b>e</b>) from March 2019 to September 2019. Black lines mark different agronomic actions and dashed lines mark the date of fertilization and only refer to the two fertilized treatments. (Treatments: 00 long-term unfertilized, D0 first-time unfertilized, 0D first-time fertilized, and DD long-term fertilized).</p>
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<p>Ammonium-N (<b>a</b>) and nitrate-N dynamics (<b>b</b>) and soil moisture (<b>c</b>) of all four treatments related to the topsoil layer (0–15 cm) from October 2019 to September 2020. Nitrous oxide emissions (<b>d</b>), error bars illustrate the standard deviation) and temperature (red line) and precipitation (<b>e</b>) from October 2019 to September 2020. Black lines mark different agronomic actions and dashed lines mark the date of fertilization and only refer to the two fertilized treatments (DD and 0D). (Treatments: 00 long-term unfertilized, D0 first-time unfertilized, 0D first-time fertilized, and DD long-term fertilized).</p>
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<p>Ammonium-N (<b>a</b>) and nitrate-N dynamics (<b>b</b>) and soil moisture (<b>c</b>) of all four treatments related to the topsoil layer (0–15 cm) from October 2020 to September 2021. Nitrous oxide emissions (<b>d</b>), error bars illustrate the standard deviation) and temperature (red line) and precipitation (<b>e</b>) from October 2020 to September 2021. Black lines mark different agronomic actions and dashed lines mark the date of fertilization and only refer to the two fertilized treatments (DD and 0D). (Treatments: 00 long-term unfertilized, D0 first-time unfertilized, 0D first-time fertilized, and DD long-term fertilized).</p>
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<p>Ammonium-N (<b>a</b>) and nitrate-N dynamics (<b>b</b>) and soil moisture (<b>c</b>) of all four treatments related to the topsoil layer (0–15 cm) from October 2021 to September 2022. Nitrous oxide emissions (<b>d</b>), error bars illustrate the standard deviation) and temperature (red line) and precipitation (<b>e</b>) from October 2021 to September 2022. Black lines mark different agronomic actions and dashed lines mark the date of fertilization and only refer to the two fertilized treatments (DD and 0D). (Treatments: 00 long-term unfertilized, D0 first-time unfertilized, 0D first-time fertilized, and DD long-term fertilized).</p>
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