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Agronomy
Volume 12
Issue 7
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Agronomy, Volume 12, Issue 7 (July 2022) – 247 articles

Cover Story (view full-size image): Black locust is largely distributed throughout the world as planted and naturalized trees, thanks to its adaptability to different environmental conditions. Introduced in Italy in 1662, in the botanical garden of Padua, it is currently naturalized throughout the whole territory and plays a significant role in national honey production, with it being an excellent melliferous plant. The temperature-based model for the simulation of black locust phenology presented here relies on the phenological observations gathered by the Italian PHEnological Network (IPHEN) from 2010 to 2021. The satisfactory model performances will provide useful information for the management of nomadic beekeeping. This tool can also help to understand the impact of climate changes on different environments, thanks to the black locust’s ubiquitous distribution. View this paper
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17 pages, 19098 KiB  
Article
Estimation of Leaf Area Index and Above-Ground Biomass of Winter Wheat Based on Optimal Spectral Index
by Zijun Tang, Jinjin Guo, Youzhen Xiang, Xianghui Lu, Qian Wang, Haidong Wang, Minghui Cheng, Han Wang, Xin Wang, Jiaqi An, Ahmed Abdelghany, Zhijun Li and Fucang Zhang
Agronomy 2022, 12(7), 1729; https://doi.org/10.3390/agronomy12071729 - 21 Jul 2022
Cited by 42 | Viewed by 4784
Abstract
Leaf area index (LAI) and above-ground biomass are both vital indicators for evaluating crop growth and development, while rapid and non-destructive estimation of crop LAI and above-ground biomass is of considerable significance for crop field management. Owing to the advantages of repeatable and [...] Read more.
Leaf area index (LAI) and above-ground biomass are both vital indicators for evaluating crop growth and development, while rapid and non-destructive estimation of crop LAI and above-ground biomass is of considerable significance for crop field management. Owing to the advantages of repeatable and high-throughput observations, spectral technology provides a feasible method for obtaining LAI and above-ground biomass of crops. In the present study, the spectral, LAI and above-ground biomass data of winter wheat were collected, and 7 species (14 in total) were calculated based on the original and first-order differential spectrum correlation spectral indices with LAI. Then, the correlation matrix method was used for correlation with LAI. The optimal wavelength combination was extracted, and the results were calculated as the optimal spectral index related to LAI. The calculation process of the optimal spectral index related to above-ground biomass was the same as that aforementioned. Finally, the optimal spectral index was divided into three groups of model input variables, winter wheat LAI and above-ground biomass estimation models were constructed using support vector machine (SVM), random forest (RF) and a back propagation neural network (BPNN), and the models were verified. The results show that the correlation coefficient between the highest of the optimal spectral indices, the LAI, and the above-ground biomass of winter wheat exceeded 0.6, and the correlation was good. The methods for establishing the optimal estimation models for LAI and above-ground biomass of winter wheat are all modeling methods in which the input variables are the combination of the first-order differential spectral index (combination 2) and RF. The R2 of the LAI estimation model validation set was 0.830, the RMSE was 0.276, and the MRE was 6.920; the R2 of the above-ground biomass estimation model validation set was 0.682, RMSE was 235.016, MRE was 4.336, and the accuracies of both models were high. The present research results can provide a theoretical basis for crop monitoring based on spectral technology and provide an application reference for the rapid estimation of crop growth parameters. Full article
(This article belongs to the Special Issue Current Research on Hyperspectral and Multispectral Imaging and Their Applications in Precision Agriculture)
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<p>Aerial photograph of winter wheat research area and some sampling plots in Yangling, Shaanxi.</p> Full article ">Figure 2
<p>Diagrams of architectures used in the experiments.</p> Full article ">Figure 3
<p>Correlation matrix diagram of spectral indices and leaf area index(LAI). (<b>a</b>) DI and LAI; (<b>b</b>) FDDI and LAI; (<b>c</b>) RI and LAI; (<b>d</b>) FDRI and LAI; (<b>e</b>) NDVI and LAI; (<b>f</b>) FDNDVI and LAI; (<b>g</b>) SAVI and LAI; (<b>h</b>) FDSAVI and LAI; (<b>i</b>) TVI and LAI; (<b>j</b>) FDTVI and LAI; (<b>k</b>) mSR and LAI; (<b>l</b>) FDmSR and LAI; (<b>m</b>) mNDI and LAI; (<b>n</b>) FDmNDI and LAI.</p> Full article ">Figure 3 Cont.
<p>Correlation matrix diagram of spectral indices and leaf area index(LAI). (<b>a</b>) DI and LAI; (<b>b</b>) FDDI and LAI; (<b>c</b>) RI and LAI; (<b>d</b>) FDRI and LAI; (<b>e</b>) NDVI and LAI; (<b>f</b>) FDNDVI and LAI; (<b>g</b>) SAVI and LAI; (<b>h</b>) FDSAVI and LAI; (<b>i</b>) TVI and LAI; (<b>j</b>) FDTVI and LAI; (<b>k</b>) mSR and LAI; (<b>l</b>) FDmSR and LAI; (<b>m</b>) mNDI and LAI; (<b>n</b>) FDmNDI and LAI.</p> Full article ">Figure 4
<p>Correlation matrix diagram of spectral indices and above-ground biomass. (<b>a</b>) DI and above-ground biomass; (<b>b</b>) FDDI and above-ground biomass; (<b>c</b>) RI and above-ground biomass; (<b>d</b>) FDRI and above-ground biomass; (<b>e</b>) NDVI and above-ground biomass; (<b>f</b>) FDNDVI and above-ground biomass; (<b>g</b>) SAVI and above-ground biomass; (<b>h</b>) FDSAVI and above-ground biomass; (<b>i</b>) TVI and above-ground biomass; (<b>j</b>) FDTVI and above-ground biomass; (<b>k</b>) mSR and above-ground biomass; (<b>l</b>) FDmSR and above-ground biomass; (<b>m</b>) mNDI and above-ground biomass; (<b>n</b>) FDmNDI and above-ground biomass.</p> Full article ">Figure 4 Cont.
<p>Correlation matrix diagram of spectral indices and above-ground biomass. (<b>a</b>) DI and above-ground biomass; (<b>b</b>) FDDI and above-ground biomass; (<b>c</b>) RI and above-ground biomass; (<b>d</b>) FDRI and above-ground biomass; (<b>e</b>) NDVI and above-ground biomass; (<b>f</b>) FDNDVI and above-ground biomass; (<b>g</b>) SAVI and above-ground biomass; (<b>h</b>) FDSAVI and above-ground biomass; (<b>i</b>) TVI and above-ground biomass; (<b>j</b>) FDTVI and above-ground biomass; (<b>k</b>) mSR and above-ground biomass; (<b>l</b>) FDmSR and above-ground biomass; (<b>m</b>) mNDI and above-ground biomass; (<b>n</b>) FDmNDI and above-ground biomass.</p> Full article ">Figure 5
<p>Prediction results of modeling set and validation set of winter wheat leaf area index inversion model with different input variables and modeling methods. (<b>a</b>) SVM Model input variable is combination 1; (<b>b</b>) SVM Model input variable is combination 2; (<b>c</b>) SVM Model input variable is combination 3; (<b>d</b>) RF Model input variable is combination 1; (<b>e</b>) RF Model input variable is combination 2; (<b>f</b>) RF Model input variable is combination 3; (<b>g</b>) BPNN Model input variable is combination 1; (<b>h</b>) BPNN Model input variable is combination 2; (<b>i</b>) BPNN Model input variable is combination 3.</p> Full article ">Figure 6
<p>Prediction results of modeling set and validation set of winter wheat above-ground biomass inversion model with different input variables and modeling methods (<b>a</b>) SVM Model input variable is combination 1; (<b>b</b>) SVM Model input variable is combination 2; (<b>c</b>) SVM Model input variable is combination 3; (<b>d</b>) RF Model input variable is combination 1; (<b>e</b>) RF Model input variable is combination 2; (<b>f</b>) RF Model input variable is combination 3; (<b>g</b>) BPNN Model input variable is combination 1; (<b>h</b>) BPNN Model input variable is combination 2; (<b>i</b>) BPNN Model input variable is combination 3.</p> Full article ">
13 pages, 3369 KiB  
Article
Textile Physical Barriers against the Chestnut Gall Wasp Dryocosmus kuriphilus
by Antonio J. Álvarez and Rocío M. Oliva
Agronomy 2022, 12(7), 1728; https://doi.org/10.3390/agronomy12071728 - 21 Jul 2022
Cited by 2 | Viewed by 1775
Abstract
Dryocosmus kuriphilus Yasumatsu is a species originating from China that, during the 20th century, has spread rapidly throughout many countries, affecting mainly different species of the genus Castanea spp. In fact, it is considered to be the most important pest of chestnut trees [...] Read more.
Dryocosmus kuriphilus Yasumatsu is a species originating from China that, during the 20th century, has spread rapidly throughout many countries, affecting mainly different species of the genus Castanea spp. In fact, it is considered to be the most important pest of chestnut trees (Castanea sativa Miller), causing significant production losses. The adoption of complementary measures to chemical and biological controls would contribute to the control of the pest. In this sense, the use of textile physical barriers could prevent the rapid spread of this species among the production centers. Therefore, the objective of this study is to define the characteristics of a textile that protects young plants that have been produced in nurseries. For this purpose, some commercial textiles have been selected based on the morphometric characterization of the species and these textiles have been accurately measured in order to compare their dimensions with those of the insects. Finally, tests have been carried out in order to measure the efficacy of the textiles under laboratory conditions, controlling the air velocity and the temperature. The results reveal that, in general, theoretical efficacy may not be a good predictor of practical results. A fully effective screen has been found against this species and its design characteristics can be used as a starting point for new, more optimized designs. Full article
(This article belongs to the Special Issue Selected Papers from 11th Iberian Agroengineering Congress)
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<p>Receipt of the first shipment of infested chestnut branches.</p> Full article ">Figure 2
<p>Individual of <span class="html-italic">D. kuriphilus</span> (dorsal view (<b>left</b>), lateral view (<b>right</b>)).</p> Full article ">Figure 3
<p>Microscopic image (<b>left</b>); same image analyzed by Euclides v.1.4 (<b>right</b>).</p> Full article ">Figure 4
<p>Three-dimensional representation of a hole and 3D surface area; definition of the parameters <span class="html-italic">a</span>, <span class="html-italic">c,</span> and <span class="html-italic">d</span>.</p> Full article ">Figure 5
<p>Graphic definition of the generatrix <span class="html-italic">d</span><sub>2</sub>.</p> Full article ">Figure 6
<p>Experimental device for measuring the protective efficacy of screens.</p> Full article ">
16 pages, 12365 KiB  
Article
Sowing Date Regulates the Growth and Yield of Broomcorn Millet (Panicum miliaceum L.): From Two Different Ecological Sites on the Loess Plateau of China
by Yan Luo, Xiangwei Gong, Jiajia Liu, Yang Qu and Baili Feng
Agronomy 2022, 12(7), 1727; https://doi.org/10.3390/agronomy12071727 - 21 Jul 2022
Viewed by 1562
Abstract
A two-year experiment was conducted to determine the optimal combinations of sowing date and variety maturity using four broomcorn millet (Panicum miliaceum L.) varieties. The results showed that sowing dates had significant effects on the leaf net photosynthesis (Pn) and chlorophyll fluorescence [...] Read more.
A two-year experiment was conducted to determine the optimal combinations of sowing date and variety maturity using four broomcorn millet (Panicum miliaceum L.) varieties. The results showed that sowing dates had significant effects on the leaf net photosynthesis (Pn) and chlorophyll fluorescence and multivariate analysis showed that the effects of variety, sowing date, measuring stage and their interactions were significant in both sites. The days from seeding to maturity were strongly decreased (6–35 d) and the ratios of reproductive growth to vegetative growth were increased in V2 and V4 and decreased in V1 and V3 in Baoji and increased in all varieties in Yulin. The highest yield was Jinshu 5 in Baoji and Shaanmei 1 in Yulin, and the total average yield of Yulin (2408.3 kg ha−1) was higher than that of Baoji (1385.2 kg ha−1) and the average yield was reduced by 12.4% and 27.2% compared to BJ1 in Baoji and 15.5%, 3.6% and 12.7% compared to YL1 in Yulin. Correlation analysis showed that the key meteorological factors which limit the growth and yield of broomcorn millet were different for the two sites. Moreover, linear fitting analysis indicated that the accumulated temperature and the number of growth days in the reproductive growth stage (R2 = 0.5306 and 0.5139) and accumulated temperature during the whole growth period (R2 = 0.4323) were the top three factors affecting the yield in Baoji and precipitation (R2 = 0.386) affected the yield in Yulin. Overall, the results of this study determined that the varieties of broomcorn millet with a short growth period should have delayed sowing, while those with a longer growth period are suitable for early sowing in the semi-arid area. Full article
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<p>Daily average temperature and precipitation from April to October in Baoji (<b>A</b>) and Yulin (<b>B</b>).</p> Full article ">Figure 2
<p>The leaf net photosynthesis (Pn) of broomcorn millet under different treatments in Baoji (<b>A</b>) and Yulin (<b>B</b>). V and S represent the variety and measuring stage, BJ and YL represent the sowing date in Baoji and Yulin, respectively. This applies to the other figures. The different lowercases in (<b>A</b>,<b>B</b>) represent the significant differences among treatments in each group at the two sites.</p> Full article ">Figure 3
<p>The overall difference in leaf chlorophyll fluorescence characteristics of broomcorn millet in Baoji (<span class="html-italic">Fv</span>/<span class="html-italic">Fm</span>, NPQ and <span class="html-italic">Φ</span><sub>PSⅡ</sub>, (<b>A</b>,<b>C</b>,<b>E</b>), respectively) and Yulin (<span class="html-italic">Fv</span>/<span class="html-italic">Fm</span>, NPQ and <span class="html-italic">Φ</span><sub>PSⅡ</sub>, (<b>B</b>,<b>D</b>,<b>F</b>), respectively). The different lowercases represent the significant differences among treatments in each group at the two sites.</p> Full article ">Figure 4
<p>The overall difference in grain size parameters of broomcorn millet (<b>A</b>,<b>B</b>,<b>C</b>). Grain length and width were showed in (<b>A</b>), grain diameters indexes (length/width, diameter and roundness) were showed in (<b>B</b>) and grain surface area and grain circumference were showed in (<b>C</b>). BJ and YL represent for sowing date in Baoji and Yulin, respectively; the different lowercases in (<b>A</b>), (<b>B</b>), and (<b>C</b>) represent the significant differences among treatments in each group at two sites.</p> Full article ">Figure 5
<p>The correlations between plant and grain parameters and meteorological factors and growth periods in Baoji (<b>A</b>) and Yulin (<b>B</b>). R<sub>VG</sub> represents the vegetative growth period; R<sub>RG</sub> represents the reproductive growth period; R<sub>V/R</sub> represents the ratio of vegetative growth period to reproductive growth period. W width, A grain area, R grain roundness, L grain length, L/W grain length/width, D grain diameter, C grain circumference, respectively. These apply to other figures and tables. * and ** represent significant correlations at the levels of <span class="html-italic">p</span> &lt; 0.05 and <span class="html-italic">p</span> &lt; 0.01.</p> Full article ">Figure 6
<p>The linear fitting relationship between yield and sunshine duration (<b>A1</b>–<b>A3</b> and <b>B1</b>–<b>B3</b>), accumulated temperature (<b>A4</b>–<b>A6</b> and <b>B4</b>–<b>B6</b>), the number of growth days (<b>A7</b>–<b>A9</b> and <b>B7</b>–<b>B9</b>) and precipitation (<b>A10</b> and <b>B10</b>) during the growing period in Baoji (<b>A</b>) and Yulin (<b>B</b>).</p> Full article ">
12 pages, 298 KiB  
Review
Breeding for Rice Aroma and Drought Tolerance: A Review
by Cyprien Ndikuryayo, Alexis Ndayiragije, Newton Kilasi and Paul Kusolwa
Agronomy 2022, 12(7), 1726; https://doi.org/10.3390/agronomy12071726 - 21 Jul 2022
Cited by 7 | Viewed by 3948
Abstract
Aroma is one of the main characteristics that guide rice purchases worldwide. Aromatic rice varieties are generally less adapted to biotic and abiotic stresses. Among the abiotic constraints, drought stress causes considerable yield losses. This review describes advances in breeding for aroma and [...] Read more.
Aroma is one of the main characteristics that guide rice purchases worldwide. Aromatic rice varieties are generally less adapted to biotic and abiotic stresses. Among the abiotic constraints, drought stress causes considerable yield losses. This review describes advances in breeding for aroma and drought tolerance in rice and investigates the possibility of combing these traits in one variety. Some of the major quantitative trait loci that have been discovered for drought tolerance were recently introgressed into aromatic varieties. However, more details on the performance of developed lines are still needed. Furthermore, there are not yet any published reports on the release of aromatic drought-tolerant rice varieties. Full article
(This article belongs to the Collection Abiotic Stress Tolerance in Plants: Towards a Sustainable Agriculture)
17 pages, 1992 KiB  
Article
Tillage and Urea Fertilizer Application Impacts on Soil C Fractions and Sequestration
by Bonginkosi S. Vilakazi, Rebecca Zengeni and Paramu Mafongoya
Agronomy 2022, 12(7), 1725; https://doi.org/10.3390/agronomy12071725 - 21 Jul 2022
Cited by 5 | Viewed by 2086
Abstract
Conservation tillage has been considered a smart agriculture practice which preserves soil organic carbon (SOC). However, little work on the labile C fractions in South Africa has been documented. As such, this work focused on C fractions under different management systems. The objective [...] Read more.
Conservation tillage has been considered a smart agriculture practice which preserves soil organic carbon (SOC). However, little work on the labile C fractions in South Africa has been documented. As such, this work focused on C fractions under different management systems. The objective of this study was to assess the impact of different tillage techniques and fertilizer application rates on soil C fractions along the soil profile. Samples from no-till (NT), conventional tillage after 5th season (CT-Y5), and annual conventional tillage, longer than 5 years (CT-ANNUAL) at 0, 60, 120, and 240 kg N ha−1 were taken at 0–10, 10–20, and 20–30 cm depths and analyzed for C fractions. The 30 cm depth was chosen as the sampling depth because of the 30 cm plough layer. At 0–10 cm, soil NT had higher total C, organic C, particulate organic C (POC), and permanganate oxidizable C (POxC) for all application rates, especially in the control treatment, compared to both the CT-Y5 and CT-ANNUAL treatments (p < 0.05). At the 10–20 cm soil depth, CT-Y5 had higher POC than both NT and CT-ANNUAL at 60 kg N ha−1 (p < 0.05). Greater C fractions in the surface soil under NT, and at deeper depths under CT, was due to litter availability on the surface under NT and incorporation to the subsoil on CT. Higher C sequestration in NT than in CT-Y5 and CT-ANNUAL was observed because of slower organic matter (OM) turnover in NT leading to the formation and stabilization of C. A larger input over output of OM, through high crop residue accumulation over decomposition, is the reason for the increase of C fractions in the fertilized treatments. Therefore, using conservation agriculture, particular NT, with 0 kg N ha−1 application rate in dryland agriculture is recommended. Full article
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<p>The experimental site, shaded with grey on the map, at Loskop in Estcourt, KwaZulu Natal Province, South Africa.</p> Full article ">Figure 2
<p>Total carbon variation with soil depths for three tillage techniques.</p> Full article ">Figure 3
<p>Organic carbon with depths and urea fertilizer application (kg N ha<sup>−1</sup>) for three tillage techniques.</p> Full article ">Figure 4
<p>Particulate organic carbon with soil depths and fertilizer application (kg N ha<sup>−1</sup>) for three tillage techniques.</p> Full article ">Figure 5
<p>Permanganate oxidizable C with soil depths and urea fertilizer application (kg N/ha) for three tillage techniques.</p> Full article ">Figure 6
<p>Microbial biomass C with soil depths and urea application (kg N ha<sup>−1</sup>) for three tillage techniques.</p> Full article ">Figure 7
<p>Variation of the microbial quotient of different soil depths.</p> Full article ">
16 pages, 2623 KiB  
Article
Adaptability and Stability Analysis of Commercial Cultivars, Experimental Hybrids and Lines under Natural Fall Armyworm Infestation in Zimbabwe Using Different Stability Models
by Prince M. Matova, Casper N. Kamutando, Bruce Mutari, Cosmos Magorokosho and Maryke Labuschagne
Agronomy 2022, 12(7), 1724; https://doi.org/10.3390/agronomy12071724 - 21 Jul 2022
Cited by 2 | Viewed by 1867
Abstract
Fall armyworm (Spodoptera frugiperda (J.E. Smith); FAW)-resistant cultivars and breeding lines have been identified in sub-Saharan Africa. However, these genotypes have not been evaluated for their stability across environments with natural FAW infestation. The objectives of this study were to: (i) identify [...] Read more.
Fall armyworm (Spodoptera frugiperda (J.E. Smith); FAW)-resistant cultivars and breeding lines have been identified in sub-Saharan Africa. However, these genotypes have not been evaluated for their stability across environments with natural FAW infestation. The objectives of this study were to: (i) identify hybrids/open pollinated varieties combining high grain yield (GYD) and stability across environments with natural FAW infestation, (ii) select maize inbred lines with high GYD and stable FAW resistance, and (iii) identify the most discriminating environments for GYD performance and foliar FAW damage (FFAWD) under natural FAW infestation. The additive main effect and multiplicative interaction (AMMI) model was used to detect the presence of genotype-by-environment interaction (GEI) for GYD, and foliar and ear FAW damage. Seven stability analysis models were used to analyse adaptation and stability of genotypes across environments. The hybrids Mutsa-MN521 and CimExp55/CML334 were the best, combining adaptation and stability across FAW infested environments. Other acceptable hybrids were identified as 113WH330, Manjanja-MN421, CML338/CML334 and PAN53. The local inbred lines SV1P and CML491 combined adaptability and stable FAW resistance across environments. The best exotic donor lines exhibiting stable FAW resistance were CML67, CML346, CML121 and CML338. Harare and Gwebi were identified as the most discriminating sites for GYD performance, while Kadoma and Rattray-Arnold Research Stations were identified for FFAWD among inbred lines. Full article
(This article belongs to the Section Crop Breeding and Genetics)
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<p>GGE biplots showing adaptation and stability of genotypes across nine environments. (<b>A</b>) A comparison GGE biplot (genotype scaling) showing adaptation and stability of genotypes across nine environments, (<b>B</b>) A ranking GGE biplot showing the mean grain yield performance of 26 maize hybrids/OPV produced across nine environments in Zimbabwe. Genotypes are identified by a code prefixed by an ‘x’. Environments are identified by a number prefixed by a ‘+E’: +E1 = Harare-DR&amp;SS-2019; +E2 = Harare-CIMMYT-2019; +E3 = Gwebi-2019; +E4 = Chisumbanje-2019; +E5 = Panmure-2019; +E6 = Rattray-Arnold-2020; +E7 = Gwebi-2020; +E8 = Chiredzi-2020; +E9 = Harare-DR&amp;SS-2020.</p> Full article ">Figure 2
<p>Covariance percentage and coefficient of regression biplots showing adaptation and stability of genotypes across environments. (<b>A</b>) A biplot showing covariance percentage (CV%) against mean grain yield performance of 26 maize genotypes evaluated across nine environments in Zimbabwe. (<b>B</b>) A graphical presentation of the Eberhart and Russel coefficient of regression (bi) vs. variability (S<sup>2</sup>di) of mean grain yield for 26 maize genotypes evaluated across nine environments in Zimbabwe. The numbers represent the genotypes evaluated and these correspond with the genotype codes in <a href="#agronomy-12-01724-t001" class="html-table">Table 1</a>.</p> Full article ">Figure 3
<p>Biplots showing the positions of genotypes and environments on the scatter-gram. (<b>A</b>) A comparison AMMI scatter biplot for grain yield (environment scaling) showing the positions of the 26 genotypes and nine environments on the two dimensional scatter-gram, (<b>B</b>) A GGE biplot showing mega-environments depicted by grain yield scores of 26 maize genotypes evaluated across nine environments in Zimbabwe during 2019–2020 seasons. Genotypes are identified by a genotype number (for <a href="#agronomy-12-01724-f003" class="html-fig">Figure 3</a>A) and a genotype code (for <a href="#agronomy-12-01724-f003" class="html-fig">Figure 3</a>B) prefixed by an ‘x’ all shown in <a href="#agronomy-12-01724-t005" class="html-table">Table 5</a>. Environments are identified by a number prefixed by a ‘+E’, +E1 = Harare-DR&amp;SS-2019; +E2 = Harare-CIMMYT-2019; +E3 = Gwebi-2019; +E4 = Chisumbanje-2019; +E5 = Panmure-2019; +E6 = Rattray-Arnold-2020; +E7 = Gwebi-2020; +E8 = Chiredzi-2020; +E9 = Harare-DR&amp;SS-2020.</p> Full article ">Figure 4
<p>Biplots showing the positions of inbred lines and environments on the scatter-gram. (<b>A</b>) A GGE scatter biplot (environment scaling) showing the position of environments and genotypes on the biplot, (<b>B</b>) A GGE biplot showing mega environments depicted by foliar fall armyworm damage scores of 26 maize genotypes evaluated across ten environments in Zimbabwe during 2019–2020 seasons. Genotypes are identified by a number prefixed by an ‘x’, the numbers represent genotypes evaluated, the numbers correspond to the genotype codes in <a href="#agronomy-12-01724-t005" class="html-table">Table 5</a>. Environments: +1 = Harare-DR&amp;SS-2019; +2 = Harare-CIMMYT-2019; +3 = Harare-Managed FAW-2019; +4 = Gwebi-2019; +5 = Rattray-Arnold-2020; +6 = Gwebi-2020; +7 = Kadoma-2020; +8 = Chiredzi-2020; +9 = Harare-Managed FAW-2020; +10 = Harare-DR-SS-2020.</p> Full article ">
20 pages, 2112 KiB  
Article
Floristic Association of Moist Temperate Forests of Shangla District, Delineated by a Multivariate Approach
by Javed Iqbal, Nasiruddin Shaikh, Moinuddin Ahmed, Wajid Zaman, Adam Khan, Asma Ayaz, Diaa O. El-Ansary, Hanoor Sharma, Hosam O. Elansary and SeonJoo Park
Agronomy 2022, 12(7), 1723; https://doi.org/10.3390/agronomy12071723 - 21 Jul 2022
Cited by 2 | Viewed by 2155
Abstract
Multivariate analysis was conducted to explore the moist temperate forests of the Shangla district, Khyber Pakhtunkhwa. The prime objective was to quantitatively describe and differentiate the vegetation groups and the factors that determine the boundaries and composition of plant communities in the Shangla [...] Read more.
Multivariate analysis was conducted to explore the moist temperate forests of the Shangla district, Khyber Pakhtunkhwa. The prime objective was to quantitatively describe and differentiate the vegetation groups and the factors that determine the boundaries and composition of plant communities in the Shangla district. This was achieved by sampling all common species in a complex vegetation mosaic coinciding with local gradients in topography and soil distribution. Ward’s clustering dendrogram demonstrated four significant vegetation clusters with respect to environmental effects. These four major groups of the tree vegetation were superimposed on the ordination plane: 1. Pinus wallichiana, the dominant group associated with Abies pindrow; 2. Abies pindrow and the Picea smithiana group; 3. Dominant Cedrus deodara associated with the Pinus wallichiana, Abies pindrow, Picea smithiana, and Quercus baloot group; 4. Pinus roxberghii pure group. The key controlling factors for each group were the environmental characteristics (i.e., edaphic factors, topographic factors, soil physical properties, and soil nutrients). The results revealed elevation (p <0.001) to be the prominent factor in the composition of plant communities. Furthermore, pH, soil moisture, maximum water holding capacity, and soil physical properties (sand, silt, and clay) also showed a significant (p < 0.05) relationship with vegetation. The other environmental factor did not show a significant relationship with vegetation. Ward’s cluster dendrogram of understory species also demonstrated four groups. Group 1 comprises two subgroups, a and b, with the highest number of species, i.e., Digeteria sanguinalis, Fragaria nubicola, Verbascum Thapsus, Pinus wallichiana seedlings, and Polygonatum multiflorium, respectively. The second large group contains twenty-five species out of eight stands, and the dominant species was Tagetis minuta. Eighteen species out of six stands were found in group 3, which was considered the smallest group. Group 4 consisted of seven stands containing twenty-four species of ground flora, with Anaphalis scopulosa followed by Adiantum venustum as the dominant species. The environmental characteristics of the understory vegetation showed a resemblance with the tree communities. With the exception of elevation, the other factors did not show a significant correlation. Full article
(This article belongs to the Special Issue Recent Progress in Plant Taxonomy and Floristic Studies)
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<p>Land cover map of the study area.</p> Full article ">Figure 2
<p>Dendrogram derived from Ward’s cluster analysis using the importance value of tree species from the Shangla district. The different colors indicate different floristic composition with respect to their environmental variables. (St indicates different stands in groups of vegetation.)</p> Full article ">Figure 3
<p>PCA stand ordination based on IVI of tree species from the Shangla district, Pakistan. The floristic composition/group obtained from Ward’s cluster analysis were super imposed on ordination axes. The different colors in the above ordination plan indicate different groups. (St indicates different stands in groups of vegetation.)</p> Full article ">Figure 4
<p>Ward’s cluster analysis of the understory vegetation based on frequency. The understory species indicates five distinct groups. (St indicates different stands in groups of vegetation.)</p> Full article ">Figure 5
<p>NMS stand ordination of the understory species, based on the frequency from the Shangla district, Pakistan. The four distinct groups (G1–G4) and the subgroup (G1a) obtained from Ward’s cluster analysis of understory species were clearly imposed on ordination plan. (St indicates different stands in groups of vegetation.)</p> Full article ">
14 pages, 3789 KiB  
Article
Effects of Microbial Fertilizer on Soil Fertility and Alfalfa Rhizosphere Microbiota in Alpine Grassland
by Yangan Zhao, Guangxin Lu, Xin Jin, Yingcheng Wang, Kun Ma, Haijuan Zhang, Huilin Yan and Xueli Zhou
Agronomy 2022, 12(7), 1722; https://doi.org/10.3390/agronomy12071722 - 21 Jul 2022
Cited by 11 | Viewed by 3119
Abstract
Chemical fertilizers are gradually being replaced with new biological fertilizers, which can improve the soil and soil microorganisms. In this experiment, leguminous forage (Medicago sativa cv. Beilin 201) was used as the research object. By measuring alfalfa root systems and soil properties [...] Read more.
Chemical fertilizers are gradually being replaced with new biological fertilizers, which can improve the soil and soil microorganisms. In this experiment, leguminous forage (Medicago sativa cv. Beilin 201) was used as the research object. By measuring alfalfa root systems and soil properties and using high-throughput sequencing technology, we investigated the effect of biological (rhizobial) fertilizer at different concentrations on soil fertility and alfalfa rhizosphere microbiota in alpine grasslands. The results demonstrated that the treatment with biofertilizer significantly reduced total nitrogen (TN) and total organic carbon (TOC) content in soils, increased root densities, and significantly increased the number of root nodules in alfalfa. There were differences in the response of rhizosphere microorganisms to different concentrations of biofertilizer, and the treatment with biofertilizer led to pronounced changes in the microbial community structure. The abundance of beneficial bacteria such as Rhizobium, Arthrobacter, and Pseudomonas was significantly increased. The Pearson correlation analysis showed that soil moisture and soil conductivity were significantly positively correlated with the observed richness of rhizosphere microbiota (p < 0.05). Meanwhile, Actinobacteria showed a significantly positive correlation with nitrate, TOC, and TN (p < 0.01). These results indicated that biofertilizers enhanced soil fertility and altered the rhizosphere microbiota of alfalfa in alpine grassland. Full article
(This article belongs to the Collection Agro-Ecology for Grassland-Based Farming Systems)
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<p>Distribution of study areas.</p> Full article ">Figure 2
<p>Schematic diagram of sample plot distribution sampling.</p> Full article ">Figure 3
<p>Photographs of the root system of the biofertilizer gradient. Note: The photos are A1, A2, A3, and A4 from left to right.</p> Full article ">Figure 4
<p>Photograph of alfalfa root tumor shape. Note: From left to right, ineffective rhizoma, effective rhizoma coral-like, effective rhizoma globular, effective rhizoma lumpy, and effective rhizoma rod-like.</p> Full article ">Figure 5
<p>Sample Venn diagram. Note: (<b>a</b>) is the fungal Venn Diagram, (<b>b</b>) is the bacterial Venn Diagram.</p> Full article ">Figure 6
<p>Soil microbiota horizontal community composition and chord diagram. Note: (<b>a</b>) represents fungal “phylum level”, (<b>b</b>) represents bacterial “phylum level”.</p> Full article ">Figure 7
<p>Abundance of community composition at the soil microbiota genus level. Note: (<b>a</b>) represents bacterial abundance heat map, (<b>b</b>) represents fungal abundance heat map</p> Full article ">
17 pages, 309 KiB  
Article
Fermentation Quality of Silages Produced from Wilted Sown Tropical Perennial Grass Pastures with or without a Bacterial Inoculant
by John W. Piltz, Richard G. Meyer, Mark A. Brennan and Suzanne P. Boschma
Agronomy 2022, 12(7), 1721; https://doi.org/10.3390/agronomy12071721 - 21 Jul 2022
Cited by 3 | Viewed by 2170
Abstract
High growth rates and rapid reproductive development and associated decline in feed quality of sown tropical perennial grass pastures present management challenges for livestock producers. Conservation of surplus forage as silage could be an effective management tool. Experiments were conducted to evaluate the [...] Read more.
High growth rates and rapid reproductive development and associated decline in feed quality of sown tropical perennial grass pastures present management challenges for livestock producers. Conservation of surplus forage as silage could be an effective management tool. Experiments were conducted to evaluate the fermentation quality of silages produced from tropical grasses. Five species (Chloris gayana, Megathyrsus maximus, Panicum coloratum, Digitaria eriantha and Cenchrus clandestinus) were ensiled without additives after a short, effective wilt at dry matter (DM) contents ranging from 302.4 to 650.1 g kg−1. The fermentation profile of all silages in 2019 was typical for high DM silages, but in 2020 ammonia (% of total nitrogen: NH3-N), acetic acid and pH levels were higher. In 2020 M. maximus (302.4 g kg−1 DM) was poorly preserved with 20.2% NH3-N. The DM content of all other silages exceeded 350 g kg−1 and fermentation quality was generally good. In a second experiment, M. maximus was ensiled at 365 g kg−1 chopped and 447 g kg−1 DM chopped and unchopped, either without or with Pioneer 1171® (Lactobacillus plantarum and Enterococcus faecium) or Lallemand Magniva Classic® (L. plantarum and Pediococcus pentasaceus) bacterial inoculant. Inoculants increased lactic acid production, reduced pH and improved fermentation compared to Control, but D-lactate, L-lactate and acetic acid production differed between inoculants. Unchopped silages had higher pH and NH3-N and better preserved protein fraction than chopped silages at the same DM content. In both experiments, wilting increased water soluble carbohydrates by 0.5–31.5 g kg−1 DM and ensiling increased degradation of the protein fraction. We concluded that a rapid and effective wilt combined with a bacterial additive resulted in well preserved tropical grass silages. Full article
(This article belongs to the Special Issue Research Progress and Future Perspectives of Silage)
18 pages, 3394 KiB  
Article
Simulation Parameter Calibration and Test of Typical Pear Varieties Based on Discrete Element Method
by Guiju Fan, Siyu Wang, Wenjie Shi, Zhenfeng Gong and Ming Gao
Agronomy 2022, 12(7), 1720; https://doi.org/10.3390/agronomy12071720 - 21 Jul 2022
Cited by 13 | Viewed by 2132
Abstract
To improve the accuracy of discrete element simulation parameters for the mechanized picking and collection of pears, the study calibrated the simulation parameters of pears by the method of combining a physical experiment and simulation. Based on the intrinsic parameters of four kinds [...] Read more.
To improve the accuracy of discrete element simulation parameters for the mechanized picking and collection of pears, the study calibrated the simulation parameters of pears by the method of combining a physical experiment and simulation. Based on the intrinsic parameters of four kinds of pears (Snow pears, Crisp pears, Huangguan pears and Qiuyue pears), their simulation models were constructed by the Hertz-Mindlin with a bonding model. The simulation parameters between pears and the contact material (PVC, EVA foam material) were calibrated by the methods of free fall collision, inclined sliding and rolling, respectively. The experiments of pear accumulation angle were carried out. It was obtained to process the image of pears with Matrix Laboratory software. In order to determine the optimal value interval of influencing factors of the pear accumulation angle, the steepest ascent experiment was carried out. Considering the coefficient of collision recovery, the coefficient of static friction and the coefficient of rolling friction between pears, five-level simulation experiments of the pear accumulation angle were designed for each factor by the method of orthogonal rotation combination. The regression model of the error between the measured value and the simulated value of the pear accumulation angle was established, and the influence of three factors on the pear accumulation angle was analyzed. The results showed that the static friction coefficient and rolling friction coefficient between pears have significant effects on the pear accumulation angle. Therefore, the optimal model of minimum error was constructed according to constraint condition, and the coefficient of collision recovery, coefficient of static friction and coefficient of rolling friction between pears were obtained. The accumulation angle verification experiments were carried out by the method of bottomless barrel lifting. The results showed that the relative error between the simulated and measured accumulation angle of four kinds of pears were 1.42%, 1.68%, 2.19% and 1.83%, respectively, which indicated that the calibrated simulation parameters were reliable. The research can provide a basis for the design and parameters optimization of harvesting machinery of pears. Full article
(This article belongs to the Special Issue Advances in Modelling Cropping Systems to Improve Yield and Quality)
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<p>Outer contour curve of Snow pear.</p> Full article ">Figure 2
<p>Geometric models of typical pears. Snow pear (<b>a</b>); Crisp pear (<b>b</b>); Qiuyue pear (<b>c</b>); Huangguan pear (<b>d</b>). Note: Blue is the origin of the coordinate system for the pear geometric model.</p> Full article ">Figure 3
<p>EDEM simulation models of typical pears. Snow pear (<b>a</b>); Crisp pear (<b>b</b>); Qiuyue pear (<b>c</b>); Huangguan pear (<b>d</b>).</p> Full article ">Figure 4
<p>Calibration experiment of collision recovery coefficient between pears and materials. Experiment principle (<b>a</b>); Physical experiment (<b>b</b>); Simulation experiment (<b>c</b>).</p> Full article ">Figure 5
<p>Calibration experiment of static friction coefficient between pears and materials. Physical experiment (<b>a</b>); Simulation experiment (<b>b</b>).</p> Full article ">Figure 6
<p>Calibration experiment of rolling friction coefficient between pears and materials. Physical experiment (<b>a</b>); Simulation experiment (<b>b</b>).</p> Full article ">Figure 7
<p>Calibration experiment of collision recovery coefficient between typical pears.</p> Full article ">Figure 8
<p>Experiment of pear accumulation angle. Physical experiment (<b>a</b>); Simulation experiment (<b>b</b>).</p> Full article ">Figure 9
<p>Experiment of simulation parameters verification. Physical experiment (<b>a</b>); Simulation experiment (<b>b</b>).</p> Full article ">Figure 10
<p>Fitting curve of the collision recovery coefficient and the maximum rebound height.</p> Full article ">Figure 11
<p>Fitting curve of static friction coefficient and inclination angle.</p> Full article ">Figure 12
<p>Fitting curve of rolling friction coefficient and rolling distance.</p> Full article ">Figure 13
<p>Processing the image of pears with Matrix Laboratory software. Original image (<b>a</b>); Grayscale processing (<b>b</b>); Boundary outline (<b>c</b>); Linear fitting (<b>d</b>). Note: The black line is the contour boundary of the pear accumulation angle, and the red line is the fitting line.</p> Full article ">
21 pages, 6623 KiB  
Article
Research on the Hydrological Variation Law of the Dawen River, a Tributary of the Lower Yellow River
by Yan Li, Long Zhao, Zhe Zhang, Jianxin Li, Lei Hou, Jingqiang Liu and Yibing Wang
Agronomy 2022, 12(7), 1719; https://doi.org/10.3390/agronomy12071719 - 21 Jul 2022
Cited by 11 | Viewed by 2062
Abstract
The natural runoff mechanism of the Dawen River, the main tributary of the lower Yellow River, has been stressed in recent years as a result of human activity, and the hydrological situation has changed dramatically. In this paper, various hydrological statistical methods such [...] Read more.
The natural runoff mechanism of the Dawen River, the main tributary of the lower Yellow River, has been stressed in recent years as a result of human activity, and the hydrological situation has changed dramatically. In this paper, various hydrological statistical methods such as the Mann–Kendall nonparametric test, cumulative anomaly, ordered clustering, sliding T test, and rainfall–runoff double-cumulative curve were used to study the evolution characteristics of hydrological factors in Dawen River. The result revealed that the rainfall and runoff of the Dawen River decreased overall from 1956 to 2016, but the downward trend was not clear, and the runoff variance was high, with 1978 as the variation point. The IHA/RVA and PCA were used to comprehensively evaluate the hydrological variability of the Dawen River, and nine representative indicators were screened out. The overall change was 58%, which is mild, and the difference in hydrological change between the IHA index system and the PCA index system was just 7%, which was predictable. The hydrological situation of the Dawen River has undergone huge changes, and there has been a serious dry-off phenomenon since 1978. The biology, habitat, and structure of the Dawen River have all been irreversibly impacted by changes in its hydrological regime. Furthermore, the key influencing aspect of hydrological variation is the vast building of water conservation schemes. The findings could serve as a theoretical foundation for integrated water resource management and ecological conservation. Full article
(This article belongs to the Special Issue Water-Saving in Agriculture: From Soil to Plant)
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<p>Geographical map of the study area.</p> Full article ">Figure 2
<p>Precipitation (<b>a</b>) and streamflow (<b>b</b>) trend map of Dawen River Daicun Dam Basin.</p> Full article ">Figure 3
<p>Spatial distribution of the annual precipitation trend from the MK test in the middle and upper reaches of the Dawen River.</p> Full article ">Figure 4
<p>Precipitation–streamflow double-accumulative curve at Daicun Dam Hydrological Station.</p> Full article ">Figure 5
<p>Correlation coefficients among the 33 IHA statistics.</p> Full article ">Figure 6
<p>Eigenvalues and cumulative contribution rates for principal component analysis.</p> Full article ">Figure 7
<p>Correlation coefficient between 9 preferred indicators.</p> Full article ">Figure 8
<p>Monthly median streamflow.</p> Full article ">Figure 9
<p>Median streamflow for January, May, and June.</p> Full article ">Figure 10
<p>7-day maximum (<b>a</b>) and 3-day minimum (<b>b</b>).</p> Full article ">Figure 11
<p>Maximum streamflow date.</p> Full article ">Figure 12
<p>High pulse count.</p> Full article ">Figure 13
<p>Fall rate (<b>a</b>) and number of reversals (<b>b</b>).</p> Full article ">Figure 14
<p>Distribution of new reservoirs in the Dawen River Basin during the study period.</p> Full article ">
12 pages, 1772 KiB  
Article
Alternate Wetting and Drying Irrigation Reduces P Availability in Paddy Soil Irrespective of Straw Incorporation
by Fanxuan Kong, Xintan Zhang, Yonghe Zhu, Haishui Yang and Fengmin Li
Agronomy 2022, 12(7), 1718; https://doi.org/10.3390/agronomy12071718 - 21 Jul 2022
Cited by 2 | Viewed by 1996
Abstract
Crop production is highly impacted by soil phosphorus (P) availability which is poor and susceptibly affected by soil moisture. However, how water management and straw incorporation affect paddy soil P availability is still not well known. A 40-day incubation experiment was conducted to [...] Read more.
Crop production is highly impacted by soil phosphorus (P) availability which is poor and susceptibly affected by soil moisture. However, how water management and straw incorporation affect paddy soil P availability is still not well known. A 40-day incubation experiment was conducted to evaluate the effects of two water management regimes: continuous flooding irrigation (CF) and alternate wetting and drying irrigation (AWD) combined with different straw addition rates (equivalent to 0, 50%, 100%, 200%, and 300% straw incorporation rates in field) on P availability in paddy soil. Water management significantly affected soil available P, microbial biomass P, total reductant, and ferrous iron. However, straw addition showed no effect on soil P availability in the short term. Compared to CF, AWD consistently decreased the soil available P content under straw addition at different rates. The main reason was that AWD increased microbial biomass for immobilizing P and decreased ferrous iron content for increasing soil P absorption, reducing available P content. In conclusion, AWD reduces available P content in paddy soil compared to CF. Water management has a more significant regulatory effect on soil P availability than straw incorporation in the field management. Full article
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<p>Average soil water content (three replicates) in alternate wetting and drying irrigation treatments at three sampling periods. SA, straw addition rate. DDP, day of the drying period.</p> Full article ">Figure 2
<p>Fresh and air-dried soil available P content and differences of available P content between air-dried soil and fresh soil under different water management regimes and straw addition rates. Different letters indicate significantly different at <span class="html-italic">p</span> &lt; 0.05 under the same water management regime. Data in the figures indicate means of three replicates ± standard deviation. ** means <span class="html-italic">p</span> &lt; 0.01; *** means <span class="html-italic">p</span> &lt; 0.001; ns means non-significant. CF, continuous flooding irrigation; AWD, alternate wetting and drying irrigation; SA, straw addition rate. DDP, day of the drying period.</p> Full article ">Figure 3
<p>Soil pH under different water management regimes and straw addition rates. Different letters indicate significantly different at <span class="html-italic">p</span> &lt; 0.05 under the same water management regime. Data in the figures indicate means of three replicates ± standard deviation. * means <span class="html-italic">p</span> &lt; 0.05; ** means <span class="html-italic">p</span> &lt; 0.01; *** means <span class="html-italic">p</span> &lt; 0.001; ns means nonsignificant. CF, continuous flooding irrigation; AWD, alternate wetting and drying irrigation; SA, straw addition rate. DDP, day of the drying period.</p> Full article ">Figure 4
<p>Soil microbial biomass P content under different water management regimes and straw addition rates. Different letters indicate significantly different at <span class="html-italic">p</span> &lt; 0.05 under the same water management regime. Data in the figures indicate means of three replicates ± standard deviation. * means <span class="html-italic">p</span> &lt; 0.05; ** means <span class="html-italic">p</span> &lt; 0.01; *** means <span class="html-italic">p</span> &lt; 0.001; ns means non-significant. CF, continuous flooding irrigation; AWD, alternate wetting and drying irrigation; SA, straw addition rate. DDP, day of the drying period.</p> Full article ">Figure 5
<p>Soil total reductant content under different water management regimes and straw addition rates. Different letters indicate significantly different at <span class="html-italic">p</span> &lt; 0.05 under the same water management regime. Data in the figures indicate means of three replicates ± standard deviation. *** means <span class="html-italic">p</span> &lt; 0.001; ns means non-significant. CF, continuous flooding irrigation; AWD, alternate wetting and drying irrigation; SA, straw addition rate. DDP, day of the drying period.</p> Full article ">Figure 6
<p>Soil ferrous iron content under different water management regimes and straw addition rates. Different letters indicate significantly different at <span class="html-italic">p</span> &lt; 0.05 under the same water management regime. Data in the figures indicate means of three replicates ± standard deviation. * means <span class="html-italic">p</span> &lt; 0.05; ** means <span class="html-italic">p</span> &lt; 0.01; *** means <span class="html-italic">p</span> &lt; 0.001; ns means non-significant. CF, continuous flooding irrigation; AWD, alternate wetting and drying irrigation; SA, straw addition rate. DDP, day of the drying period.</p> Full article ">Figure 7
<p>Relationships between soil water content and available P, pH, microbial biomass P, total reductant, and ferrous iron under alternate wetting and drying irrigation.</p> Full article ">
13 pages, 710 KiB  
Communication
From an Introduced Pulse Variety to the Principal Local Agricultural Industry: A Case Study of Red Kidney Beans in Kelan, China
by Jiliang Ma, Nawab Khan, Jin Gong, Xiaopeng Hao, Xuzhen Cheng, Xin Chen, Jianwu Chang and Huijie Zhang
Agronomy 2022, 12(7), 1717; https://doi.org/10.3390/agronomy12071717 - 21 Jul 2022
Cited by 6 | Viewed by 3470
Abstract
The development of introduced pulse varieties has made valuable contributions to the development of the global agricultural industry, and China is one of the largest pulse producers in the international market. A special type of pulse, the red kidney bean, has made a [...] Read more.
The development of introduced pulse varieties has made valuable contributions to the development of the global agricultural industry, and China is one of the largest pulse producers in the international market. A special type of pulse, the red kidney bean, has made a major contribution to improving the rural economy. Taking Kelan County, Shanxi Province, as an example, this paper expounds on the formation of the kidney bean industry and its impact on local development. The existing research used a qualitative case study (QCS) method to examine the driver and impact of kidney beans in the agricultural industry. This study found that (1) the development of the kidney bean industry has benefited from its adherence to a market demand-oriented strategy, focusing on breeding and retaining excellent varieties, and vigorously supporting the construction of technical systems and the cultivation of the main body of the industrial chain. Developing new varieties, creating brands, and industrial integration are the key driving forces for development. (2) The kidney bean industry promotes local development by increasing farmers’ income, forming a more complete kidney bean supply chain, highlighting the brand effect, and promoting sustainable rural development. This study suggests that disease-resistant and mechanized-adapted varieties need to be developed in the future. Market and demand trends should be constantly monitored when determining reproductive paths. Full article
(This article belongs to the Special Issue Cultivar Development of Pulses Crop)
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<p>Five different stages of kidney bean development in KL County.</p> Full article ">Figure 2
<p>Mechanism of the kidney beans’ industrial development.</p> Full article ">
13 pages, 30274 KiB  
Article
Characterization of the MADS-Box Gene CmFL3 in chrysanthemum
by Kunkun Zhao, Song Li, Diwen Jia, Xiaojuan Xing, Haibin Wang, Aiping Song, Jiafu Jiang, Sumei Chen, Fadi Chen and Lian Ding
Agronomy 2022, 12(7), 1716; https://doi.org/10.3390/agronomy12071716 - 20 Jul 2022
Cited by 4 | Viewed by 2009
Abstract
Chrysanthemummorifolium is one of the four major cut flowers in the world, with high ornamental and economic value. Flowering time is an important ornamental characteristic of chrysanthemum that affects its value in the market. In Arabidopsis, the FRUITFULL (FUL) gene [...] Read more.
Chrysanthemummorifolium is one of the four major cut flowers in the world, with high ornamental and economic value. Flowering time is an important ornamental characteristic of chrysanthemum that affects its value in the market. In Arabidopsis, the FRUITFULL (FUL) gene plays a key role in inducing flowering. Here, we isolated an FUL clade MADS-box gene, CmFL3, from chrysanthemum inflorescence buds. CmFL3 localized in the cellular membrane and nucleus, and showed no transcriptional activity in yeast. The qRT-PCR assay showed that CmFL3 was strongly expressed in the leaves, receptacles, and disc floret petals. Furthermore, CmFL3 was mainly detected in the inflorescence meristem and bract primordia using in situ hybridization. Similar to Arabidopsis, overexpression of CmFL3 in chrysanthemum induced early flowering. Particularly, the expression level of CmAFT was downregulated, whereas that of CmFTL3 was upregulated in the leaves of transgenic chrysanthemum lines. Meanwhile, the overexpression of CmFL3 in Arabidopsis also led to earlier flowering. Furthermore, the expression of AtFT, AtAP1, AtLFY, and AtFUL was significantly increased in CmFL3 transgenic Arabidopsis. The present study verified the function of CmFL3 in regulating flowering time and further revealed that it could affect the expression of other flowering-related genes—CmAFT and CmFTL3. Therefore, the CmFL3 gene may be an important candidate for genetic breeding aimed at regulating flowering. Full article
(This article belongs to the Special Issue Frontier Studies in Genetic Breeding of Ornamental Plants)
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<p>Sequence analysis of CmFL3. (<b>A</b>) Sequence alignment of CmFL3 and homologous proteins. The black color indicates 100% identity; red, 80% identity. Gaps are shown by dots; FUL motif is shown by the black box, and other key motifs are underlined. (<b>B</b>) Phylogenetic tree of AP1/FUL-like subfamily proteins and CmFL3 (black box). Detailed information is shown in the <a href="#app1-agronomy-12-01716" class="html-app">Supplementary Materials</a>. The GenBank accession number of CmFL3 is ON959211.</p> Full article ">Figure 2
<p>Transcriptional profiling of <span class="html-italic">CmFL3</span>. The relative expression level of <span class="html-italic">CmFL3</span> in (<b>A</b>) vegetative stage and (<b>B</b>) reproductive stage under short-day conditions quantified using qRT-PCR. In situ localization of <span class="html-italic">CmFL3</span> in (<b>C</b>) inflorescence bud (diameter &lt; 2 mm). Rt, root; Ste, stem; Le, leaf; Sa, shoot apex; Rpe, ray floret petal; Rpi, ray floret pistil; Dpe, disc floret petal; Dpi, disc floret pistil; Dst, disc floret stamen; IM, inflorescence meristem; FM, flower meristem; Br, bract; Re, receptacle. The IM, FM, and Br (<b>C</b>) are indicated by red arrows. Values (<b>A</b>,<b>B</b>) represent mean ± SE (<span class="html-italic">n</span> = 3). Significant differences were analyzed using Duncan’s multiple range test. Different letters above the bars represent significant differences (<span class="html-italic">p</span> &lt; 0.05). Bar = 200 μm.</p> Full article ">Figure 3
<p>Subcellular localization of CmFL3. GFP, green fluorescence protein; DIC, differential interference contrast; Merged, overlay plots. Bars = 50 μm.</p> Full article ">Figure 4
<p>Transcriptional activity of CmFL3. (<b>A</b>) Transcriptional activity of CmFL3 in yeast. pCL1 and pGBKT7 are the positive and negative controls, respectively. -AH: SD/-His-Ade lacking x-α-gal; -AH+x-α-gal: SD/-His-Ade containing x-α-gal. (<b>B</b>) Arabidopsis mesophyll protoplasts captured after luciferin addition using a charge-coupled device camera. (<b>C</b>) Luciferase activity measured after the introduction of 35S::GAL4DB-CmFL3 into Arabidopsis mesophyll protoplasts. Values are represented as mean ± SE (<span class="html-italic">n</span> = 3). Significant differences were analyzed using Duncan’s multiple range test, and bars with different letters are significantly different from each other (<span class="html-italic">p</span> &lt; 0.05).</p> Full article ">Figure 5
<p>Phenotypes of <span class="html-italic">CmFL3</span> transgenic ‘Jinba’ plants. (<b>A</b>) RT-PCR analysis of <span class="html-italic">CmFL3</span> in WT plants and Ox lines at the cDNA level; the forward primer was from pMDC-43 vector and reverse primer from the CDS. (<b>B</b>) The relative expression levels of <span class="html-italic">CmFL3</span> in the WT and Ox transgenic plants were determined using qRT-PCR. <span class="html-italic">CmEF1a</span> was the control gene. The values represent means ± SE (<span class="html-italic">n</span> = 3). Significant differences were determined using Student’s <span class="html-italic">t</span>-test (** <span class="html-italic">p</span> &lt; 0.01). Each sample included three biological and three technical replicates. (<b>C</b>) Flowering phenotype of WT and Ox transgenic lines. The photographs were captured at 116 d after transplantation. (<b>D</b>) Development of inflorescence in WT ‘Jinba’ and Ox plants. FM stage, flower meristem stage; FBD stage, flower bud development stage; VC stage, visible color stage; EO stage, early opening stage; OF stage, opened flower stage; FO, fully opened flower stage; SF stage, senescing flower stage. Bars = 3.0 cm in the upper panel, whereas bars = 13.5 cm in the lower panel. Bar = 3 cm in (<b>D</b>) except for the FM stage, where bar = 500 μm.</p> Full article ">Figure 6
<p>Relative expression levels of flowering-related genes in the WT plants and Ox transgenic lines. (<b>A</b>,<b>B</b>) The relative expression of <span class="html-italic">CmAFT</span> and <span class="html-italic">CmFTL3</span> in the leaves of WT and Ox plants. Values represent means ± SE (<span class="html-italic">n</span> = 3), and the significant differences were determined using Student’s <span class="html-italic">t</span> test (* <span class="html-italic">p</span> &lt; 0.05).</p> Full article ">Figure 7
<p>Phenotypic observation of the WT and <span class="html-italic">CmFL3</span>-overexpressing transgenic Arabidopsis plants. (<b>A</b>) The abundance of <span class="html-italic">CmFL3</span> in the transgenic and WT plants. (<b>B</b>) The number of days to bolting. (<b>C</b>) The number of rosette leaves produced at bolting. (<b>D</b>) The phenotype of the plants at bolting. The values represent means ± SE (<span class="html-italic">n</span> = 15), and the significant differences were determined using Student’s <span class="html-italic">t</span> test (* <span class="html-italic">p</span> &lt; 0.05). Bars = 1 cm.</p> Full article ">
20 pages, 2343 KiB  
Article
Are Traditional Lima Bean (Phaseolus lunatus L.) Landraces Valuable to Cope with Climate Change? Effects of Drought on Growth and Biochemical Stress Markers
by M. Isabel Martínez-Nieto, Sara González-Orenga, Pilar Soriano, Josefa Prieto-Mossi, Elena Larrea, Antonio Doménech-Carbó, Ana Maria Tofei, Oscar Vicente and Olga Mayoral
Agronomy 2022, 12(7), 1715; https://doi.org/10.3390/agronomy12071715 - 20 Jul 2022
Cited by 7 | Viewed by 2767
Abstract
Agrobiodiversity and adaptability to environmental changes derived from global warming are challenges for the future of agriculture. In this sense, landraces often have high levels of genetic variation, tightly connected with the changing environmental conditions of a territory. The genus Phaseolus, with [...] Read more.
Agrobiodiversity and adaptability to environmental changes derived from global warming are challenges for the future of agriculture. In this sense, landraces often have high levels of genetic variation, tightly connected with the changing environmental conditions of a territory. The genus Phaseolus, with five domesticated species, is one of the most important sources of proteins, carbohydrates and micronutrients in various countries. This study aimed to compare the adaptation capacity to drought, in the vegetative growth phase, of a commercial cultivar and two landraces traditionally cultivated in the Mediterranean basin of Phaseolus lunatus (Lima bean). Growth and biochemical responses of the analysed genotypes to different water-deficit treatments were evaluated and compared. In addition, the effectiveness of the voltammetric method for evaluating stress levels in cultivated plants was tested. The studied parameters revealed that P. lunatus is a drought-tolerant species, showing similar results for the three cultivars. However, contrary to what was expected from the germination phase results, the commercial variety Peru showed some better responses under water stress conditions. Finally, the voltammetric method proved to be a good and fast tool for assessing oxidative stress in cultivated plants, showing results in agreement with total phenolic compounds and total flavonoid fluctuations. Full article
(This article belongs to the Collection Abiotic Stress Tolerance in Plants: Towards a Sustainable Agriculture)
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<p>Cyclic voltammogram after semi-derivative convolution of a microparticulate deposit of the ethanolic extract of sample A<sup>−5</sup> on GCE in contact with air-saturated 0.25 M HAc/NaAc aqueous buffer at pH 4.75. Potentials can initiate at 0.0 V vs. Ag/AgCl in the positive direction; potential scan rate 50 mV s<sup>−1</sup>.</p> Full article ">Figure 2
<p>Effect of water stress on plant growth parameters of the three <span class="html-italic">Phaseolus lunatus</span> cultivars expressed as percentage reduction with respect to their respective control (60–80% of soil moisture), where FW is ‘fresh weight’ and WC ‘water content’: stem length (<b>a</b>), number of leaves (<b>b</b>), aerial part FW (<b>c</b>), root FW (<b>d</b>), aerial part WC (<b>e</b>) and root WC (<b>f</b>). Absolute values stem length, Peru: 5.55 cm, Pintat: 5.82 cm and Ull de Perdiu: 5.51 cm. Absolute values number of leaves: Peru: 24.8, Pintat 26 and Ull de Perdiu: 36.4. Absolute values FW aerial part: Peru: 52.45 gr, Pintat: 67.23 gr and Ull de Perdiu: 60.33 gr. Absolute values FW roots: Peru: 11.40 gr, Pintat: 17.21 gr and Ull de Perdiu: 12.71 gr. Lowercase letters indicate significant differences within each cultivar and uppercase letters (in bold) between cultivars, but within treatments, according to Tukey’s test (α = 0.05). <span class="html-italic">p</span> values according to one-way ANOVA.</p> Full article ">Figure 3
<p>Effect of water stress on total fresh weight for the three <span class="html-italic">Phaseolus lunatus</span> cultivars expressed as percentage reduction with respect to their respective control (60–80% soil moisture). Absolute values of total FW: Peru: 63.84 gr, Pintat: 84.44 gr and Ull de Perdiu: 73.05 gr. Lowercase letters indicate significant differences within each cultivar and uppercase letters (in bold) between cultivars, but within treatments, according to Tukey’s test (α = 0.05). <span class="html-italic">p</span> values according to one-way ANOVA.</p> Full article ">Figure 4
<p>Chlorophyll a (Chl a) (<b>a</b>) chlorophyll b (Chl b) (<b>b</b>) and carotenoid (Caro) (<b>c</b>) contents in leaves of the three <span class="html-italic">Phaseolus lunatus</span> cultivars. Lowercase letters indicate significant differences within each cultivar and uppercase letters (in bold) between cultivars, but within treatments, according to Tukey’s test (α = 0.05). <span class="html-italic">p</span> values according to one-way ANOVA.</p> Full article ">Figure 5
<p>Leaf content of proline in the three <span class="html-italic">Phaseolus lunatus</span> cultivars. Lowercase letters indicate significant differences within each cultivar and uppercase letters (in bold) between cultivars, but within treatments, according to Tukey’s test (α = 0.05). <span class="html-italic">p</span> values according to one-way ANOVA.</p> Full article ">Figure 6
<p>Leaf content of malondialdehyde in the three <span class="html-italic">Phaseolus lunatus</span> cultivars. Lowercase letters indicate significant differences within each cultivar and uppercase letters (in bold) between cultivars, but within treatments, according to Tukey’s test (α = 0.05). <span class="html-italic">p</span> values according to one-way ANOVA.</p> Full article ">Figure 7
<p>Leaf contents of total phenolic compounds (TPC) (<b>a</b>) and total flavonoids (TF) (<b>b</b>) in the three <span class="html-italic">Phaseolus lunatus</span> cultivars. Lowercase letters indicate significant differences within each cultivar and uppercase letters (in bold) between cultivars, but within treatments, according to Tukey’s test (α = 0.05). <span class="html-italic">p</span> values according to one-way ANOVA.</p> Full article ">Figure 8
<p>Detail of the region between −0.6 and 1.0 V vs. Ag/AgCl in cyclic voltammograms, after semi-derivative convolution, of microparticulate deposits of ethanolic extracts of samples from Peru (<b>a</b>,<b>b</b>), Pintat (<b>c</b>,<b>d</b>) and Ull de Perdiu (<b>e</b>,<b>f</b>) cultivars, in control (<b>a</b>,<b>c</b>,<b>e</b>) and maximum drought stress (<b>b</b>,<b>d</b>,<b>f</b>). From voltammograms such as in <a href="#agronomy-12-01715-f001" class="html-fig">Figure 1</a>. The dotted lines mark the baselines used to measure the intensity of 0.67 V (<span class="html-italic">I</span><sub>670</sub>) and 0.0 V (<span class="html-italic">I</span><sub>0</sub>) peak currents, which provide information on the differences between cultivars in relation to ROS scavenging capacity. A higher difference between the ratios <span class="html-italic">I</span><sub>670</sub>/<span class="html-italic">I</span><sub>0</sub> of control and stressed plants denotes a higher capacity and, therefore, lower oxidative damage under maximum drought conditions.</p> Full article ">Figure 9
<p>Variation of the <span class="html-italic">I</span><sub>520</sub>/<span class="html-italic">I</span><sub>670</sub> peak current ratio determined in voltammograms such as in <a href="#agronomy-12-01715-f002" class="html-fig">Figure 2</a> (from 3–5 replicate measurements) with the percentage of water stress for Peru (circles), Pintat (solid circles) and Ull the Perdiu (triangles).</p> Full article ">
15 pages, 997 KiB  
Article
Phenotypic Variability, Heritability and Associations of Agronomic and Quality Traits in Cultivated Ethiopian Durum Wheat (Triticum turgidum L. ssp. Durum, Desf.)
by Temesgen Dagnaw, Behailu Mulugeta, Teklehaimanot Haileselassie, Mulatu Geleta and Kassahun Tesfaye
Agronomy 2022, 12(7), 1714; https://doi.org/10.3390/agronomy12071714 - 20 Jul 2022
Cited by 12 | Viewed by 2909
Abstract
Quality is an important aspect of durum wheat in the processing sector. Thus, recognizing the variability of quality and agronomic traits and their association is fundamental in designing plant breeding programs. This study aimed to assess the variability, heritability, genetic advance, and correlation [...] Read more.
Quality is an important aspect of durum wheat in the processing sector. Thus, recognizing the variability of quality and agronomic traits and their association is fundamental in designing plant breeding programs. This study aimed to assess the variability, heritability, genetic advance, and correlation of some agronomic and quality traits among 420 Ethiopian durum wheat genotypes and to identify the promising genotypes with distinct processing quality attributes to produce superior quality pasta. The field experiment was conducted at two locations (Sinana and Chefe Donsa) using an alpha lattice design with two replications. Analysis of variance, chi-square test, and Shannon–Weaver diversity index revealed the existence of highly significant (p < 0.001) variation among genotypes for all studied traits. The broad-sense heritability values were ranging from 46.2% (days to maturity) to 81% (thousand kernel weight) with the genetic advance as a percent of the mean ranging from 1.1% (days to maturity) to 21.2% (grain yield). The phenotypic correlation coefficients for all possible pairs of quantitative traits showed a significant (p < 0.05) association among most paired traits. The gluten content (GC) and grain protein content (GPC) were negatively correlated with grain yield and yield-related traits and positively associated with phenological traits, while yield and phenological traits correlated negatively. The frequency distributions of amber-colored and vitreous kernels, which are preferable characters of durum wheat in processing, were highly dominant in Ethiopian durum wheat genotypes. The identified top 5% genotypes, which have amber color and vitreous kernel with high GC and GPC content as well as sufficient grain yield, could be directly used by the processing sector and/or as donors of alleles in durum wheat breeding programs. Full article
(This article belongs to the Special Issue Crop Landraces: Resources, Conservation, and Utilization)
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<p>Map of Ethiopia showing the sample collection sites of landrace accessions and field trial sites.</p> Full article ">Figure 2
<p>Phenotypic (above diagonal) correlation coefficients, distribution (diagonal), and scatterplot with regression curve (below diagonal) of 11 quantitative traits for the 420 durum wheat genotypes. Critical values for Pearson’s correlation coefficient are 0.1002 at 0.05%, 0.13 at 0.01% and 0.17 at 0.001. “***” = significant at <span class="html-italic">p</span> &lt; 0.001; “**” = significant at <span class="html-italic">p</span> &lt; 0.01; “*” = significant at <span class="html-italic">p</span> &lt; 0.05.</p> Full article ">
18 pages, 2484 KiB  
Article
Hydromulching Enhances the Growth of Artichoke (Cynara cardunculus var. scolymus) Plants Subjected to Drought Stress through Hormonal Regulation of Source–Sink Relationships
by Miriam Romero-Muñoz, Amparo Gálvez, Purificación A. Martínez-Melgarejo, María Carmen Piñero, Francisco M. del Amor, Alfonso Albacete and Josefa López-Marín
Agronomy 2022, 12(7), 1713; https://doi.org/10.3390/agronomy12071713 - 20 Jul 2022
Cited by 2 | Viewed by 1755
Abstract
Mulching the soil with organic-based formulations (hydromulching) is a sustainable alternative to plastic mulching that is here hypothesized to maintain crop production under drought stress by hormonal and metabolic regulation of source–sink relationships. To test this hypothesis, artichoke plants were grown on non-mulched [...] Read more.
Mulching the soil with organic-based formulations (hydromulching) is a sustainable alternative to plastic mulching that is here hypothesized to maintain crop production under drought stress by hormonal and metabolic regulation of source–sink relationships. To test this hypothesis, artichoke plants were grown on non-mulched soil and on soil mulched with polyethylene and three different organic mixtures, and subjected to optimal and reduced irrigation regimes. Under drought stress, the growth parameters were higher in plants grown with the different mulching treatments compared to non-mulched plants, which was related to a higher photosynthetic rate and water-use efficiency. Importantly, mulching-associated growth improvement under stress was explained by higher sucrolytic activity in the leaves that was accompanied by a decline in the active cytokinins. Besides this, salicylic acid decreased in the leaves, and abscisic acid and the ethylene precursor 1-aminocyclopropane-1-carboxylic acid were impaired in the artichoke heads, which is associated with better regulation of photoassimilate partitioning. Taken together, these results help to explain the hydromulching-associated growth improvement of artichokes under water stress through the hormonal regulation of sucrose metabolism, which could be very useful in future breeding programs for drought tolerance. Full article
(This article belongs to the Section Crop Breeding and Genetics)
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<p>(<b>a</b>) Net CO<sub>2</sub> fixation rate (Amax), (<b>b</b>) stomatal conductance (gs), (<b>c</b>) transpiration rate (E), and (<b>d</b>) water-use efficiency (WUEi) in artichoke plants of the commercial Symphony variety non-mulched or subjected to different mulching treatments and cultivated under control (well-watered) and water-stress (70% ETc) conditions. Bars show the means of five plants ± standard error. Different capital letters indicate significant differences due to the water-stress treatment, while different small letters show significant differences among mulching treatments according to Tukey’s test (<span class="html-italic">p</span> ≤ 0.05). Abbreviations used: bare soil (BS), polyethylene mulch (PE), mushroom-substrate-based hydromulch (MS), rice-hull-based hydromulch (RH), and wheat-straw-based hydromulch (WS).</p> Full article ">Figure 2
<p>Hexose (glucose + fructose) concentrations in (<b>a</b>) leaves and (<b>b</b>) heads, and sucrose concentrations in (<b>c</b>) leaves and (<b>d</b>) heads of artichoke plants of the commercial Symphony variety non-mulched or subjected to different mulching treatments and cultivated under control (well-watered) and water-stress (70% ETc) conditions. Bars show the means of five plants ± standard error. Different capital letters indicate significant differences due to the water-stress treatment, while different small letters show significant differences among mulching treatments according to Tukey’s test (<span class="html-italic">p</span> ≤ 0.05). Abbreviations used: bare soil (BS), polyethylene mulch (PE), mushroom-substrate-based hydromulch (MS), rice-hull-based hydromulch (RH), and wheat-straw-based hydromulch (WS).</p> Full article ">Figure 3
<p>Sucrolytic activity in (<b>a</b>) leaves and (<b>b</b>) heads of artichoke plants of the commercial Symphony variety non-mulched or subjected to different mulching treatments and cultivated under control (well-watered) and water-stress (70% ETc) conditions. Bars show the means of five plants ± standard error. Different capital letters indicate significant differences due to the water-stress treatment, while different small letters show significant differences among mulching treatments according to Tukey’s test (<span class="html-italic">p</span> ≤ 0.05). Abbreviations used: bare soil (BS), polyethylene mulch (PE), mushroom-substrate-based hydromulch (MS), rice-hull-based hydromulch (RH), and wheat-straw-based hydromulch (WS).</p> Full article ">Figure 4
<p>(<b>a</b>,<b>b</b>) Trans-zeatin (tZ), (<b>c</b>,<b>d</b>) zeatin riboside (ZR), and (<b>e</b>,<b>f</b>) isopentenyladenine (iP) in leaves and heads of artichoke plants of the commercial Symphony variety non-mulched or subjected to different mulching treatments and cultivated under control (well-watered) and water-stress (70% ETc) conditions. Bars show the means of five plants ± standard error. Different capital letters indicate significant differences due to the water-stress treatment, while different small letters show significant differences among mulching treatments according to Tukey’s test (<span class="html-italic">p</span> ≤ 0.05). Abbreviations used: bare soil (BS), polyethylene mulch (PE), mushroom-substrate-based hydromulch (MS), rice-hull-based hydromulch (RH), and wheat-straw-based hydromulch (WS).</p> Full article ">Figure 5
<p>(<b>a</b>,<b>b</b>) Gibberellin A4 (GA4), (<b>c</b>,<b>d</b>) indoleacetic acid (IAA), (<b>e</b>,<b>f</b>) abscisic acid (ABA), (<b>g</b>,<b>h</b>) 1-aminocyclopropane-1-carboxylic acid (ACC), (<b>i</b>,<b>j</b>) jasmonic acid (JA) and (<b>k</b>,<b>l</b>) salicylic acid (SA) in leaves and heads of artichoke plants of the commercial Symphony variety non-mulched or subjected to different mulching treatments and cultivated under control (well-watered) and water-stress (70% ETc) conditions. Bars show the means of five plants ± standard error. Different capital letters indicate significant differences due to the water-stress treatment, while different small letters show significant differences among mulching treatments according to Tukey’s test (<span class="html-italic">p</span> ≤ 0.05). Abbreviations used: bare soil (BS), polyethylene mulch (PE), mushroom-substrate-based hydromulch (MS), rice-hull-based hydromulch (RH), and wheat-straw-based hydromulch (WS).</p> Full article ">Figure 6
<p>(<b>a</b>) Bi-plot representing the score values and (<b>b</b>,<b>c</b>) two axes of a principal component (PC1, PC2) analysis showing the loadings of various growth-related, ionic, sucrose metabolism, and hormonal variables (denoted by abbreviations) of artichoke plants of the commercial Symphony variety non-mulched or subjected to different mulching treatments and cultivated under control (well-watered) and water-stress (70% ETc) conditions. Circles enclose those variables/scores which cluster together in score-PCA and loading-PCA. Abbreviations used: boron (B), calcium (Ca), copper (Cu), iron (Fe), potassium (K), magnesium (Mg), manganese (Mn), sodium (Na), phosphorus (P), chloride (Cl<sup>−</sup>), sulfate (SO4<sup>2−</sup>), plant height (PHeight), plant diameter (PDiameter), number of leaves (NLeaves), edible part fresh weight (FWedible), chlorophyll a (Chl a), chlorophyll b (Chl b), total chlorophylls (Total Clh), net CO<sub>2</sub> fixation rate (A), stomatal conductance (gs), transpiration rate (E), intrinsic water-use efficiency (WUEi), osmotic potential (Ψs), relative water content (RWC), leaf glucose and fructose (L_GF), leaf sucrose (L_Suc), artichoke head glucose and fructose (AH_GF), artichoke head sucrose (AH_Suc), leaf sucrolytic activity (L_SucAct), artichoke head sucrolytic activity (AH_SucAct), leaf abscisic acid (L_ABA), leaf 1-aminocyclopropane-1-carboxylic acid (L_ACC), leaf indole acetic acid (L_IAA), leaf salicylic acid (L_SA), leaf jasmonic acid (L_JA), leaf gibberellin A4 (L_GA4), leaf trans-zeatin (L_tZ), leaf zeatin riboside (L_ZR), leaf isopentenyladenine (L_iP), artichoke head abscisic acid (AH_ABA), artichoke head 1-aminocyclopropane-1-carboxylic acid (AH_ACC), artichoke head indole acetic acid (AH_IAA), artichoke head salicylic acid (AH_SA), artichoke head jasmonic acid (AH_JA), artichoke head gibberellin A4 (AH_GA4), artichoke head trans-zeatin (AH_tZ), artichoke head zeatin riboside (AH_ZR), artichoke head isopentenyladenine (AH_iP), bare soil (BS), polyethylene mulch (PE), mushroom-substrate-based hydromulch (MS), rice-hull-based hydromulch (RH), and wheat-straw-based hydromulch (WS).</p> Full article ">
13 pages, 756 KiB  
Article
Agroecological Screening of Copper Alternatives for the Conservation of Soil Health in Organic Olive Production
by Alev Kir, Barbaros Cetinel, Didar Sevim, Feriste Ozturk Gungor, Francis Rayns, Dionysios Touliatos and Ulrich Schmutz
Agronomy 2022, 12(7), 1712; https://doi.org/10.3390/agronomy12071712 - 20 Jul 2022
Cited by 3 | Viewed by 3009
Abstract
The efficacy of soil conditioner (vermicompost tea), fertiliser (potassium silicate), and biological control agents (BCAs) as practical agroecological copper alternatives against olive leaf spot (Spilocaea oleaginea (Cast.) Hughe.) disease was investigated between 2018 and 2021 under organic management in a Mediterranean [...] Read more.
The efficacy of soil conditioner (vermicompost tea), fertiliser (potassium silicate), and biological control agents (BCAs) as practical agroecological copper alternatives against olive leaf spot (Spilocaea oleaginea (Cast.) Hughe.) disease was investigated between 2018 and 2021 under organic management in a Mediterranean climate. In total, 9 agroecological alternatives to copper oxychloride (vermicompost tea, potassium silicate, Bacillus subtilis EU 007 WP, Platanus orientalis leaf extract, Mycorrhiza mix, seaweed commercial product, Trichoderma citrinoviride TR1, vermicompost tea+Platanus orientalis mix, Penicillium (Mouldy bread pieces)) were applied to olive trees in a randomised block design with 4 replicationsTotal water soluble phenol compounds (TWSP) were found to be the main bioindicator to assess the alternatives and their potential to phase-out copper application. Results related to TWSP indicated that copper oxychloride (control), potassium silicate and vermicompost tea showed significantly higher content of TWSP as we compared zero application of copper and other treatments. These stimulate the antioxidant capacity in olive fruits and reduce the olive leaf spot disease incidence. The pollution effect of copper was monitored during the trial to identify soil pollution in the organic in-conversion experimental land. The total annual ‘active copper’ application was 4.7 kg.ha−1.year−1 and this is in accordance with the legal organic legislation of Turkey. During the conversion period from conventional to organic management, we determined approximately 50% reduced copper content in the soil 0–30 cm depth samples in 2020 (3.70 mg.kg−1) as it is compared to those initial samples (6.43 mg.kg−1) in 2018. We conclude that alternatives to copper that are easily accessible, e.g., vermicompost tea, have a potential for use in organic olive production to replace copper in mitigating olive leaf spots. Furthermore, we find that reduced copper application in organic management with the aim to decrease copper accumulation in soil, fruits and leaves was not yet enough to reduce copper to satisfactory levels. We conclude that further research with the aim of a total replacement of copper fungicide treatments in organic and non-organic systems is needed. Full article
(This article belongs to the Special Issue Agroecology and Organic Horticulture)
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<p>Geographic location of the experimental site (<b>a</b>) and fruits of “Domat” variety (<b>b</b>).</p> Full article ">Figure 2
<p>0–5 Scale: Appearance of symptoms on the leaf area (%).</p> Full article ">
20 pages, 2407 KiB  
Article
Genetic Pool of the Cultivated Pear Tree (Pyrus spp.) in the Canary Islands (Spain), Studied Using SSR Molecular Markers
by María Encarnación Velázquez-Barrera, Ana María Ramos-Cabrer, Santiago Pereira-Lorenzo and Domingo José Ríos-Mesa
Agronomy 2022, 12(7), 1711; https://doi.org/10.3390/agronomy12071711 - 20 Jul 2022
Cited by 9 | Viewed by 2521
Abstract
The Canary Islands have an enormous richness of crops and varieties, many of them traditional or local, selected for decades by farmers based on the most desirable characteristics. Pear trees were introduced to the Canary Islands presumably in the first years after their [...] Read more.
The Canary Islands have an enormous richness of crops and varieties, many of them traditional or local, selected for decades by farmers based on the most desirable characteristics. Pear trees were introduced to the Canary Islands presumably in the first years after their Conquest in the 15th century, reaching a high degree of diversification. In this study, to determine the genetic identity of the genus Pyrus in the Canary Islands for conservation purposes, 266 pear accessions from the islands of Tenerife, La Palma and Gran Canaria were characterized with 18 SSRs, in addition to 190 genotypes from Galicia, Asturias, wild and commercial varieties as references to detect possible synonyms, genetic relationships and the possible genetic structure. We identified 310 unique genotypes, both diploid and putative triploid, 120 of them present only in the Canary Islands (39%, with 50% clonality). The population structure of the genotypes was analyzed by STRUCTURE 2.3.4 software (Pritchard Lab, Stanford University, Stanford, CA, USA). The dendrogram, by using the Jaccard coefficient and principal component analysis (PCoA), separated the analyzed genotypes into stable groups. One of these groups was formed only by Canarian varieties present at lower altitudes, showing adaptation to low chilling requirements with a significant positive correlation (0.432, p < 0.01). This first study of the pear germplasm in the Canary Islands reflects the importance of the group of local cultivars and their need for conservation given they are adapted to their peculiar climatic conditions and have a low number of chill units. Full article
(This article belongs to the Collection Genetic Diversity Evaluation of the Fruit Trees)
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<p>Geographical locations of the 456 samples of pear tree (<span class="html-italic">Pyrus</span> spp.) included in this study. RPP1: reconstructed panmictic population unique to the Canary Islands; RPP2, RPP3: reconstructed panmictic populations identified in the northwestern Iberian Peninsula and the Canary Islands.</p> Full article ">Figure 2
<p>Tenerife genotypes, classified in RPP1-3 and admixed, and their sampling locations in Tenerife, according to the chill portions (CP) of the dynamic model (Fishman et al., 1987) [<a href="#B20-agronomy-12-01711" class="html-bibr">20</a>].</p> Full article ">Figure 3
<p>Dendogram for the 310 unique pear genotypes based on Jaccard’s coefficient with an indication of the reconstructed panmictic populations for K = 3 and 18 SSRs. The RPP number assigned by the STRUCTURE software is indicated before the genotype code/name.</p> Full article ">Figure 4
<p>Representation of principal components (PCs) of reconstructed panmictic populations (RPP1 to RPP3) obtained with STRUCTURE using 18 SSRs and 310 pear genotypes.</p> Full article ">Figure 5
<p>Three first principal components (PCs) of the PCoA for the genotypes differentiated with JC ≤ 0.13 and the RPPs using STRUCTURE for 310 pear genotypes.</p> Full article ">
12 pages, 1600 KiB  
Article
SNP Genotyping for Purity Assessment of a Forage Oat (Avena sativa L.) Variety from Colombia
by Luis Fernando Campuzano-Duque, Diego Bejarano-Garavito, Javier Castillo-Sierra, Daniel Ricardo Torres-Cuesta, Andrés J. Cortés and Matthew Wohlgemuth Blair
Agronomy 2022, 12(7), 1710; https://doi.org/10.3390/agronomy12071710 - 20 Jul 2022
Cited by 1 | Viewed by 2816
Abstract
Single nucleotide polymorphism (SNP) markers have multiple applications in plant breeding of small grains. They are used for the selection of divergent parents, the identification of genetic variants and marker-assisted selection. However, the use of SNPs in varietal purity assessment is under-reported, especially [...] Read more.
Single nucleotide polymorphism (SNP) markers have multiple applications in plant breeding of small grains. They are used for the selection of divergent parents, the identification of genetic variants and marker-assisted selection. However, the use of SNPs in varietal purity assessment is under-reported, especially for multi-line varieties from the public sector. In the case of variety evaluation, these genetic markers are tools for maintaining varietal distinctness, uniformity and stability needed for cultivar release of multi-line or pure-line varieties of inbred crops. The objective of this research was to evaluate the purity and relationships of one original (AV-25) and two multi-line sub-populations (AV25-T and AV25-S) of the inbreeding species, oats (Avena sativa L.). Both sub-populations could be useful as forages in the central highland region of Colombia (>2000 masl), such as in the departments of Boyacá and Cundinamarca, even though they were derived from an original composite mixture widely used in the mountainsides of the southern department of Nariño named Avena 25. Representative single plant selections (SPS) from the two sub-populations were grown together with SPS harvests from off-type plants (early and late) and plants from the original AV25 composite mixture, to determine their genetic similarity. Plants were genotyped by DNA extraction of a plateful of 96 individual plant samples and SNPs were detected for an Illumina Infinium 6K Chip assay. The data were used for the analysis of genetic structure and population relationships. The grouping observed based on the genetic data indicated that AV25-T and AV25-S were homogeneous populations and somewhat divergent in their genetic profile compared to the original AV25-C mix. In addition, to the two commercial, certified oat varieties (Cajicá and Cayuse) were different from these. The early and late selections were probable contaminants and could be discarded. We concluded that the use of SNP markers is an appropriate tool for ensuring genetic purity of oat varieties. Full article
(This article belongs to the Special Issue Omics Approaches for Crop Improvement)
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<p>Genetic structure of 88 plants representing seven forage oat genotypes (A = Cajicá and Cayuse, commercial varieties), B = AV25-C, the mixed source population, C = AV25-P, the early selection D = AV25-S, mass selection samples from Sutacón, E = AV25-T mass selection samples from Tibaitatá and F = AV25-Ta, the late selection. Each individual panel is divided into subgroups coded in colors based on clustering <span class="html-italic">K</span>-value from 2 to 4 being the number of groups assumed. Length of the bar segment represents the estimated proportion of sample membership.</p> Full article ">Figure 2
<p>Analysis of main components (PCoA) for estimating genetic structure patterns of forage oat genotypes used in this study: with (<b>a</b>) first two components, (<b>b</b>) first and second components, and (<b>c</b>) second and third components. The percentage of variation explained by each component is shown within parenthesis in the label of the corresponding axis. Axes are drawn to the same scale to make comparisons. Different colors represent groups.</p> Full article ">Figure 3
<p>Networks depicting (<b>a</b>) relative divergence scores (<span class="html-italic">F<sub>ST</sub></span>), and (<b>b</b>) bidirectional gene flow (computed by migrants per generation, <span class="html-italic">N<sub>e</sub>m</span>) among four forage oat genetic clusters (circles). Clusters were determined following the unsupervised clustering approaches of the previous section: where the orange (1) and blue (2) clusters included samples of the purified genotypes AV25-S (29 lines), AV25-T (30) and some of the AV25-C samples (13); the purple cluster (3) included samples of commercial varieties, and AV25-P and AV25-Ta; and the brown cluster (4) included AV25-P samples and a single AV25-S sample.</p> Full article ">
15 pages, 1565 KiB  
Article
Stoichiometry of Soil, Microorganisms, and Extracellular Enzymes of Zanthoxylum planispinum var. dintanensis Plantations for Different Allocations
by Yitong Li, Yanghua Yu and Yanping Song
Agronomy 2022, 12(7), 1709; https://doi.org/10.3390/agronomy12071709 - 19 Jul 2022
Cited by 7 | Viewed by 2143
Abstract
Plantations with different allocation patterns significantly affect soil elements, microorganisms, extracellular enzymes, and their stoichiometric characteristics. Rather than studying them as a continuum, this study used four common allocations of plantations: Zanthoxylum planispinum var. dintanensis (hereafter Z. planispinum) + Prunus salicina, [...] Read more.
Plantations with different allocation patterns significantly affect soil elements, microorganisms, extracellular enzymes, and their stoichiometric characteristics. Rather than studying them as a continuum, this study used four common allocations of plantations: Zanthoxylum planispinum var. dintanensis (hereafter Z. planispinum) + Prunus salicina, Z. planispinum + Sophora tonkinensis, Z. planispinum + Arachis hypogaea, and Z. planispinum + Lonicera japonica plantations, as well as a single-stand Z. planispinum plantation as a control. Soil samples from depths of 0–10 and 10–20 cm at the five plantations were used to analyze the element stoichiometry, microorganisms and extracellular enzymes. (1) One-way analysis of variance (ANOVA) showed that the contents of soil organic carbon (C), nitrogen (N), phosphorus (P), and potassium (K) of Z. planispinum + L. japonica plantation were high, while those of calcium (Ca) and magnesium (Mg) were low compared to the Z. planispinum pure plantation; soil microbial and enzyme activities were also relatively high. Stoichiometric analysis showed that soil quality was good and nutrient contents were high compared to the other plantations, indicating that this was the optimal plantation. (2) Two-way ANOVA showed that stoichiometry was more influenced by plantation type than soil depth and their interaction, suggesting that plantation type significantly affected the ecosystem nutrient cycle; soil microbial biomass (MB) C:MBN:MBP was not sensitive to changes in planting, indicating that MBC:MBN:MBP was more stable than soil C:N:P, which can be used to diagnose ecosystem nutrient constraints. (3) Pearson’s correlation and standardized major axis analyses showed that there was no significant correlation between soil C:N:P and MBC:MBN:MBP ratios in this study; moreover, MBN:MBP had significant and extremely significant correlations with MBC:MBN and MBC:MBP. Fitting the internal stability model equation of soil nutrient elements and soil MBC, MBN, and MBP failed (p > 0.05), and the MBC, MBN, and MBP and their stoichiometric ratios showed an absolute steady state. This showed that, in karst areas with relative nutrient deficiency, soil microorganisms resisted environmental stress and showed a more stable stoichiometric ratio. Overall stoichiometric characteristics indicated that the Z. planispinum + L. japonica plantation performed best. Full article
(This article belongs to the Special Issue Emerging Research on Adaptive Plants in Karst Ecosystems)
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<p>Soil element contents of <span class="html-italic">Z. planispinum</span> plantations for different allocations. YD1: <span class="html-italic">Z. planispinum</span> + <span class="html-italic">P. salicina</span>; YD2: <span class="html-italic">Z. planispinum</span> + <span class="html-italic">S. tonkinensis</span>; YD3: <span class="html-italic">Z. planispinum</span> + <span class="html-italic">A. hypogaea</span>; YD4: <span class="html-italic">Z. planispinum</span> + <span class="html-italic">L. japonica</span>; YD5: <span class="html-italic">Z. planispinum</span> pure plantation. The same notation is used in the other figures. Lower case letters, significant differences between different plantation types of the same depth at <span class="html-italic">p</span> &lt; 0.05; upper case letters, significant differences between different depths of the same plantation types at <span class="html-italic">p</span> &lt; 0.05.</p> Full article ">Figure 2
<p>Stoichiometry of soil elements of <span class="html-italic">Z. planispinum</span> plantations. C:N, soil C:N ratio; C:P, soil C:P ratio; N:P, soil N:P ratio; C:K, soil C:K ratio; N:K, soil N:K ratio; P:K, soil P:K ratio; C:Ca, soil C:Ca ratio; Ca:Mg, soil Ca:Mg ratio. Lower case letters, significant differences between different plantation types of the same depth at <span class="html-italic">p</span> &lt; 0.05; upper case letters, significant differences between different depths of the same plantation types at <span class="html-italic">p</span> &lt; 0.05.</p> Full article ">Figure 3
<p>Concentration of soil microorganisms of <span class="html-italic">Z. planispinum</span> plantations. Lower case letters, significant differences between different plantation types of the same depth at <span class="html-italic">p</span> &lt; 0.05; upper case letters, significant differences between different depths of the same plantation types at <span class="html-italic">p</span> &lt; 0.05.</p> Full article ">Figure 4
<p>Soil microbial biomass and stoichiometry of <span class="html-italic">Z. planispinum</span> plantations. MBC, soil microbial biomass carbon; MBN, soil microbial biomass nitrogen; MBP, soil microbial biomass phosphorus; MBC:MBN, soil microbial biomass carbon to microbial biomass nitrogen ratio; MBC:MBP, soil microbial biomass carbon to microbial biomass phosphorus ratio; MBN:MBP, soil microbial biomass nitrogen to microbial biomass phosphorus ratio. Lower case letters, significant differences between different plantation types of the same depth at <span class="html-italic">p</span> &lt; 0.05; upper case letters, significant differences between different depths of the same plantation types at <span class="html-italic">p</span> &lt; 0.05.</p> Full article ">Figure 5
<p>Soil extracellular enzymes activities (EEAs) and their stoichiometry of <span class="html-italic">Z. planispinum</span> plantations. BG, β-1,4-glucosidase; NAG, β-1,4-n-acetylglucosaminidase; LAP, leucine aminopeptidase; AP, acid phosphatase; BG:(NAG + LAP), ratio of β-1,4-glucosidase to the sum of β-1,4-n-acetylglucosaminidase and leucine aminopeptidase; BG:AP, β-1,4-glucosidase to acid phosphatase ratio; (NAG + LAP):AP, ratio of the sum of β-1,4-n-acetylglucosaminidase and leucine aminopeptidase to acid phosphatase. Lower case letters, significant differences between different plantation types of the same depth at <span class="html-italic">p</span> &lt; 0.05; upper case letters, significant differences between different depths of the same plantation types at <span class="html-italic">p</span> &lt; 0.05.</p> Full article ">Figure 6
<p>Correlations of soil element, microorganism, and extracellular enzyme C:N:P of <span class="html-italic">Z. planispinum</span> plantations. C:N, soil C:N ratio; C:P, soil C:P ratio; N:P, soil N:P ratio; MBC:MBN, soil microbial biomass carbon to microbial biomass nitrogen ratio; MBC:MBP, soil microbial biomass carbon to microbial biomass phosphorus ratio; MBN:MBP, soil microbial biomass nitrogen to microbial biomass phosphorus ratio; BG:(NAG + LAP), ratio of β-1,4-glucosidase to the sum of β-1,4-n-acetylglucosaminidase and leucine aminopeptidase; BG:AP, β-1,4-glucosidase to acid phosphatase ratio; (NAG + LAP):AP, ratio of the sum of β-1,4-n-acetylglucosaminidase and leucine aminopeptidase to acid phosphatase. *, ** indicates significant differences at <span class="html-italic">p</span> &lt; 0.05 and <span class="html-italic">p</span> &lt; 0.01, respectively.</p> Full article ">
16 pages, 9609 KiB  
Article
Research on Hydraulic Properties and Energy Dissipation Mechanism of the Novel Water-Retaining Labyrinth Channel Emitters
by Yanfei Li, Xianying Feng, Yandong Liu, Xingchang Han, Haiyang Liu, Yitian Sun, Hui Li and Yining Xie
Agronomy 2022, 12(7), 1708; https://doi.org/10.3390/agronomy12071708 - 19 Jul 2022
Cited by 11 | Viewed by 1951
Abstract
As a key component of a drip irrigation system, the performance of the drip irrigation emitters is mainly determined by the flow channel structures and structural parameters. In this study, a novel type of circular water-retaining labyrinth channel (CWRLC) structure emitter was proposed, [...] Read more.
As a key component of a drip irrigation system, the performance of the drip irrigation emitters is mainly determined by the flow channel structures and structural parameters. In this study, a novel type of circular water-retaining labyrinth channel (CWRLC) structure emitter was proposed, inspired by the effect of roundabouts that make vehicles slow down and turn. Using the single-factor experiment method, the influence of the hydraulic performance of CWRLC emitters was researched under different circular radii. The internal flow characteristics and energy dissipation mechanism were analyzed by a computational fluid dynamics (CFD) simulation. It can be seen from the analysis that the energy dissipation abilities of the flow channel depend on the proportion of low-speed vortex areas. The larger the proportion of low-speed vortex areas, the smaller the flow index of the CWRLC emitter. Quadrate water-retaining labyrinth channel (QWRLC) and stellate water-retaining labyrinth channel (SWRLC) structures were obtained by structural improvements for increasing the proportion of low-speed vortex areas. The simulation results showed that the flow indexes of two improved structural emitters were significantly decreased. CWRLC, QWRLC, SWRLC, and widely used tooth labyrinth channel (TLC) emitters were manufactured by using technologies of electrical discharge machining (EDM) and injection molding (IM). The physical test results showed that the SWRLC emitter achieved the best hydraulic performance compared with the other three emitters. Therefore, the SWRLC emitter has a broad prospect of application in water-saving irrigation. Full article
(This article belongs to the Special Issue Agricultural Water Conservation: Tools, Strategies, and Practices - Series II)
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<p>Schematic diagram of the CWRLC unit structure.</p> Full article ">Figure 2
<p>Three-dimensional physical model of the CWRLC emitter.</p> Full article ">Figure 3
<p>Processing of molds of the novel emitters using EDM.</p> Full article ">Figure 4
<p>Drip irrigation emitters flow test platform.</p> Full article ">Figure 5
<p>The flow indexes of the CWRLC emitter with different parameters <span class="html-italic">r</span>.</p> Full article ">Figure 6
<p>The mid-depth cross sectional velocity vector distributions of the fourth flow channel unit for six flow channels under 100 KPa.</p> Full article ">Figure 7
<p>The velocity line diagram of the center line of the mid-depth cross-section for six flow channels.</p> Full article ">Figure 8
<p>Schematic diagrams of two structural improvements.</p> Full article ">Figure 9
<p>Simulation results of the velocity vector distributions in two improved flow channel structures under 100 KPa.</p> Full article ">Figure 10
<p>Schematic diagram and velocity vector distribution of the flow channel structures under 100 KPa.</p> Full article ">Figure 11
<p>The outlet flow rate and inlet pressure fitted curves of the four emitters.</p> Full article ">Figure 12
<p>Four real emitters.</p> Full article ">Figure 13
<p>The histogram of the flow index of the four emitters.</p> Full article ">Figure 14
<p>The histogram of the flow index errors of the four emitters.</p> Full article ">
16 pages, 667 KiB  
Review
Innovations in Disease Detection and Forecasting: A Digital Roadmap for Sustainable Management of Fruit and Foliar Disease
by Gultakin Hasanaliyeva, Melissa Si Ammour, Thaer Yaseen, Vittorio Rossi and Tito Caffi
Agronomy 2022, 12(7), 1707; https://doi.org/10.3390/agronomy12071707 - 19 Jul 2022
Cited by 6 | Viewed by 3365
Abstract
In a quickly growing world, there is increasing demand for a secure food supply, a reduction in the intensive use of natural resources, and the enhancement of sustainability for future long-term maintenance. In this regard, plant health, including fruit and foliar diseases, which [...] Read more.
In a quickly growing world, there is increasing demand for a secure food supply, a reduction in the intensive use of natural resources, and the enhancement of sustainability for future long-term maintenance. In this regard, plant health, including fruit and foliar diseases, which can cause a vast amount of crop loss, potentially has a huge effect on food security. The integration of new, innovative technological tools and data management techniques into the traditional agricultural practices is a promising approach to combat future food shortages. The use of the same principles of precision agriculture to “do the right thing, at the right time, in the right place” will allow for providing detailed, real-time information that will help farmers to protect their crops and choose healthier, as well as more productive, farming methods. The presented narrative review reports on several items of innovation, including monitoring and surveillance, diagnostic, and decision-making tools, with a specific focus devoted to digital solutions that can be applied in agriculture in order to improve the quality and the speed of the decision-making process and specifically, to set up a digital collaboration that can be crucial under certain circumstances to reach sustainability goals, particularly in the Near East and North Africa (NENA) Region, where an effective and rapid solution for phytosanitary control is needed. Full article
(This article belongs to the Special Issue Perspectives and Challenges for Sustainable Management of Fruit and Foliar Diseases)
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<p>Digital roadmap leading to a digital collaboration era: the awareness of different digital tools available (monitoring tools—green, diagnostic tools—orange, and decision tools—blue) initiates the change from traditional farming (where the three areas are separated) to a more agile thinking (where different tools can be used, but the three areas are still separated) and finally, to a more collaborative architecture, where different tools of different areas can work together, enhancing each other.</p> Full article ">
13 pages, 2991 KiB  
Article
Functional Analysis and Precise Location of m-1a in Rice
by Qing Dong, Jia Shen, Fang Wang, Yaocheng Qi, Chaoqiang Jiang, Chaolong Zu and Tingchun Li
Agronomy 2022, 12(7), 1706; https://doi.org/10.3390/agronomy12071706 - 19 Jul 2022
Cited by 1 | Viewed by 1611
Abstract
The T-DNA insertion technique is widely used in molecular breeding for its stable inheritance and low copy number in the plant genome. In our experiment, a transfer DNA (T-DNA) insertion grain of m-1a in rice was identified. Phenotypic analysis revealed that the grains [...] Read more.
The T-DNA insertion technique is widely used in molecular breeding for its stable inheritance and low copy number in the plant genome. In our experiment, a transfer DNA (T-DNA) insertion grain of m-1a in rice was identified. Phenotypic analysis revealed that the grains appeared chalky and became extensive. The epidermis was shrinking. Meanwhile, the amylose contents of the seeds decreased significantly, and the expression of the most starch synthesis genes was obviously downregulated. Using the whole-genome sequencing and chromosome step method, the insertion position was uncovered and only located in Chr11 between 23266185 and 23266186 bp. These results may provide material for opening up new T-DNA insertion position points and a theoretical basis for rice molecular breeding. Full article
(This article belongs to the Special Issue Environmental Ecological Remediation and Farming Sustainability)
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<p>Phenotypes of transgenic rice plants. (<b>A</b>) Seed morphologies of T<sub>1</sub> transgenic rice. WT = wild-type plants (‘Zhonghua 11’), <span class="html-italic">bzip22</span> = normal transgenic rice plants of <span class="html-italic">ZmbZIP22</span>. Bar = 1 mm. (<b>B</b>) Co-segregation analysis of the grain size using T<sub>2</sub> <span class="html-italic">m-1a</span> transgenic seeds. 1–24 = T<sub>2</sub> <span class="html-italic">m-1a</span> transgenic seeds. Red dotted lines represent the size mean of wide-type seeds.</p> Full article ">Figure 2
<p>Phenotype analysis of T<sub>3</sub> <span class="html-italic">m-1a</span> plants. (<b>I</b>) Different phenotypes: (<b>a</b>) panicles, (<b>b</b>) hulls, (<b>c</b>) stamens, (<b>d</b>) pistils and (<b>e</b>) glume hairs. (<b>II</b>) Developmental stages of T<sub>3</sub> seeds. WT = wild-type plants (‘Zhonghua 11’). Bars = 1 mm.</p> Full article ">Figure 3
<p>Comparison of the size of apiculus in <span class="html-italic">m-1a</span> and wild-type rice. (<b>I</b>) Comparison of grain length and width. (<b>II</b>) Comparison of apiculus length. (<b>II</b>) Comparison of 1000-seed weight. C1–3 = <span class="html-italic">m-1a</span> plants; WT = wild-type plants (‘Zhonghua 11’). Values represent the mean ± SD of triplicates. The asterisks indicate that the correlation coefficients were significantly different (* <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01).</p> Full article ">Figure 4
<p>Comparison of the structures in <span class="html-italic">m-1a</span> and wild-type seeds. (<b>a</b>) Skin structure of brown rice grains in the light. (<b>b</b>) Cross-sections of brown rice grains in the light. (<b>c</b>) Skin structure of brown rice grains in transmission light. (<b>d</b>) Cross-sections of brown rice grains in transmission light. (<b>I)</b> Cross-sections of brown rice grains in scanning electron microscopy images. (<b>II</b>) Starch structure of endosperm center in scanning electron microscopy images. (<b>a</b>–<b>d</b>) Bar = 1 mm. (<b>I</b>) Bar = 1 mm. (<b>II</b>) Bar = 20 μm. WT = wild-type plants (‘Zhonghua 11’).</p> Full article ">Figure 5
<p>Comparison of the amylose and starch contents in <span class="html-italic">m-1a</span> rice and wild-type seeds. (<b>I</b>) Comparison of the amylose contents. (<b>II</b>) Comparison of the starch contents. 1–3 = <span class="html-italic">m-1a</span> rice plants. (WT = wild-type plants (‘Zhonghua 11’). Values represent the mean ± SD of triplicates. The asterisks indicate that the correlation coefficients were highly significantly different (* <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01).</p> Full article ">Figure 6
<p>Accurate insertion location positioning of <span class="html-italic">m-1a</span> strain. Chr11 = chromosome 11; M = Mega base pair.</p> Full article ">Figure 7
<p>Expression profiles of 18 rice starch synthesis genes during development of Zhonghua 11 (wild-type) and the <span class="html-italic">m-1a</span> strain. The gene expression in the 3-d endosperm of wild-type rice was used as a control. d = days after pollination; WT = wild type. Values represent the mean ± SD of triplicates.</p> Full article ">Figure 8
<p>Expression profiles of 3 rice genes during development of Zhonghua 11 (wild type) and the <span class="html-italic">m-1a</span> strain. The gene expression in the 3-d endosperm of wild-type rice was used as a control. d = days after pollination; WT = wild type. Values represent the mean ± SD of triplicates.</p> Full article ">
13 pages, 12969 KiB  
Article
Effect of Harvesting Time on the Chemical Composition of Cynara cardunculus L. var. altilis Blades
by Filipa Mandim, Spyridon A. Petropoulos, Celestino Santos-Buelga, Isabel C. F. R. Ferreira and Lillian Barros
Agronomy 2022, 12(7), 1705; https://doi.org/10.3390/agronomy12071705 - 19 Jul 2022
Cited by 4 | Viewed by 1731
Abstract
In the present study, the fluctuations in fatty acids, tocopherols, organic acids, and free sugars content of cardoon blades collected at sixteen harvest dates (samples B1–B16, corresponding to principal growth stages (PGS) between 1 and 9) were evaluated. A total of 26 fatty [...] Read more.
In the present study, the fluctuations in fatty acids, tocopherols, organic acids, and free sugars content of cardoon blades collected at sixteen harvest dates (samples B1–B16, corresponding to principal growth stages (PGS) between 1 and 9) were evaluated. A total of 26 fatty acids were identified, with palmitic (C16:0, 19.9–40.13%), α-linolenic (C18:3n3, 6.39–33.2%), and linoleic (C18:2n6c, 9–34.8%) acids being present in higher relative abundances in most of the samples, while lipid content was the highest in samples of late (B15) and intermediate (B8–B10) stages of maturity. The α- and γ-tocopherols were the only detected vitamin E isoforms, while α-isoform presented the highest concentration (80–8567 µg/100 g dw) in all the studied samples, except for samples B9–B11, for which the γ-tocopherol was detected in higher concentrations. Moreover, samples B1 and B14 showed the highest content of total tocopherols (8352 and 10,197 µg/100 g dw, respectively). The identified organic acids were oxalic, quinic, malic, citric, and fumaric. Malic acid was present in higher concentrations in almost all the samples analyzed, except for samples B3 and B15, in which the presence of oxalic acid stood out. Regarding the free sugar’s composition, fructose, glucose, sucrose, trehalose, and raffinose were the only detected compounds, with sucrose being present in higher concentrations in almost all the samples (1.662–10.8 g/100 g dw), while samples at younger maturation stages, namely samples B4 and B5, presented the highest concentrations of total sugars. In conclusion, the obtained results demonstrate the influence that the growth cycle may have on the chemical composition of this tissue (blades) of the species. Moreover, having a more complete knowledge regarding its composition and identifying the stage of maturation which is most appropriate for obtaining a greater amount of certain bioactive compounds will help to increase the added value of this multi-purpose crop. Full article
(This article belongs to the Special Issue Integrated Management of Crop Residues and Their Bioactive Properties in Agri-Food Production)
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<p>Fatty acids chromatogram of <span class="html-italic">Cynara cardunculus</span> L. var. <span class="html-italic">altilis</span> blades (sample B4, PGS 2). 1. C6:0—caproic acid; 2. C8:0—caprylic acid; 3. C10:0—capric acid; 4. C11:0—undecanoic acid; 5. C12:0—lauric acid; 6. C13:0—tridecanoic acid; 7. C14:0—myristic acid; 8. C14:1—tetradecanoic acid; 9. C15:0—pentadecanoic acid; 10. C16:0—palmitic acid; 11. C16:1—palmitoleic acid; 12. C17:0—heptadecanoic acid; 13. C18:0—stearic acid; 14. C18:1n9c—oleic acid; 15. C18:2n6c—linoleic acid; 16. C18:3n3—linolenic acid; 17. C20:0—arachidic acid; 18. C20:1—gadoleic acid; 19. C21:0—heneicosanoic acid; 20. C22:0—behenic acid; 21. C22:1—eicosenoic acid; 22. C20:5n3—eicosapentaenoic acid; 23. C23:0—tricosanoic acid; 24. C24:0—lignoceric acid.</p> Full article ">Figure 2
<p>Two-dimensional scatterplot of principal components 1 and 2 for the tested variables at different maturation stages of cardoon blades (samples B1–B16).</p> Full article ">Figure 3
<p>The loading plot of principal components 1 and 2 for the tested variables at different maturation stages of cardoon blades.</p> Full article ">Figure 4
<p>Two-dimensional scatterplot of principal components 1 and 3 for the tested variables at different maturation stages of cardoon blades (samples B1–B16).</p> Full article ">Figure 5
<p>The loading plot of principal components 1 and 3 for the tested variables at different maturation stages of cardoon blades.</p> Full article ">
20 pages, 1236 KiB  
Article
Effects of Daily Light Integral on Compact Tomato Plants Grown for Indoor Gardening
by Stephanie Cruz and Celina Gómez
Agronomy 2022, 12(7), 1704; https://doi.org/10.3390/agronomy12071704 - 19 Jul 2022
Cited by 9 | Viewed by 4515
Abstract
Our objective was to characterize the growth, physiological responses, fruit yield, and quality of tomato (Solanum lycopersicum L.) plants grown under different daily light integrals (DLIs) and photoperiods. In experiment I, nine compact tomato cultivars were grown indoors using broadband white light-emitting [...] Read more.
Our objective was to characterize the growth, physiological responses, fruit yield, and quality of tomato (Solanum lycopersicum L.) plants grown under different daily light integrals (DLIs) and photoperiods. In experiment I, nine compact tomato cultivars were grown indoors using broadband white light-emitting diode (LED) fixtures. Plants were grown under low (10.4 mol·m−2·d−1) and high (18.4 mol·m−2·d−1) DLIs with 12 and 16 h photoperiods, respectively, and two intermediate DLIs of 13.8 mol·m−2·d−1 with either 12 or 16 h photoperiods. In experiment II, three compact tomato cultivars were grown under the same low DLI with either 8 or 12 h photoperiods, and the same high DLI with either 12 or 16 h photoperiods. Generally, higher DLIs decreased plant growth and increased the fruit yield. Changing the DLI delivery strategy by adjusting the photoperiod and photosynthetic photon flux density (PPFD) did not have major effects on the growth, yield, and fruit quality of the compact tomato plants evaluated in this study, even though net photosynthesis increased under higher PPFDs in experiment II. Although several cultivars were affected by intumescence, only two cultivars showed treatment responses, for which the severity was generally higher in lower PPFDs using the same DLI. Full article
(This article belongs to the Special Issue Growing Food in Cities: Rooftops, Window Sills, Vertical Farms, Urban Agroecology)
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<p>Symptoms of intumescence; canker-like lesions on the surface of tomato leaves on the (<b>a</b>) abaxial and (<b>b</b>) adaxial leaf surface.</p> Full article ">Figure 2
<p>Intumescence severity of ‘Yellow Canary’ tomato in experiment I under (<b>a</b>) a low DLI of 10.4 mol·m<sup>−2</sup>·d<sup>−1</sup> and (<b>b</b>) a high DLI of 18.4 mol·m<sup>−2</sup>·d<sup>−1</sup>.</p> Full article ">
13 pages, 936 KiB  
Article
Large-Effect QTLs for Titratable Acidity and Soluble Solids Content Validated in ‘Honeycrisp’-Derived Apple Germplasm
by Baylee A. Miller, Sarah A. Kostick and James J. Luby
Agronomy 2022, 12(7), 1703; https://doi.org/10.3390/agronomy12071703 - 19 Jul 2022
Cited by 5 | Viewed by 2810
Abstract
Fruit acidity and sweetness are important fruit quality traits in the apple and are therefore targets in apple breeding programs. Multiple quantitative trait loci (QTLs) associated with titratable acidity (TA) and soluble solids content (SSC) have been previously detected. In this study a [...] Read more.
Fruit acidity and sweetness are important fruit quality traits in the apple and are therefore targets in apple breeding programs. Multiple quantitative trait loci (QTLs) associated with titratable acidity (TA) and soluble solids content (SSC) have been previously detected. In this study a pedigree-based QTL analysis approach was used to validate QTLs associated with TA and SSC in a ‘Honeycrisp’-derived germplasm set. TA and SSC data collected from 2014 to 2018 and curated genome-wide single nucleotide polymorphism (SNP) data were leveraged to validate three TA QTLs on linkage groups (LGs) 1, 8, and 16 and three SSC QTLs on LGs 1, 13, and 16. TA and SSC QTL haplotypes were characterized in six University of Minnesota apple breeding families representing eight breeding parents including ‘Honeycrisp’ and ‘Minneiska’. Six high-TA haplotypes, four low-TA haplotypes, 14 high-SSC haplotypes, and eight low-SSC haplotypes were characterized. The results of this study will enable more informed selection in apple breeding programs. Full article
(This article belongs to the Special Issue DNA-Informed Breeding in Fruit and Nut Crops)
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<p>Means and 95% confidence intervals for across-year adjusted titratable acidity (TA) for offspring functional diplotypes at the LG1, LG8, and LG16 QTLs. Low-TA haplotypes were 1E, 1F, 8I, and 16H (corresponding to designations in <a href="#agronomy-12-01703-t002" class="html-table">Table 2</a>). High-TA haplotypes were 1A, 8A, 8B, 8C, 16A, and 16B (corresponding to designations in <a href="#agronomy-12-01703-t002" class="html-table">Table 2</a>). The number of offspring with a given functional diplotype is listed below each functional diplotype.</p> Full article ">Figure 2
<p>Means and 95% confidence intervals for across-year adjusted soluble solids content (SSC) for offspring functional diplotypes at the LG1, LG13, and LG16 QTLs. Low-SSC haplotypes were 1D, 1E, 13J, 16A, 16B, 16C, and 16D (corresponding to designations in <a href="#agronomy-12-01703-t003" class="html-table">Table 3</a>). High-SSC haplotypes were 1A, 1F, 13A, 13B, 13C, 13D, 13E, 13F, 13G, 13H, 13I, 16E, 16F, and 16H (corresponding to designations in <a href="#agronomy-12-01703-t003" class="html-table">Table 3</a>). The number of offspring with a given functional diplotype is listed below each functional diplotype.</p> Full article ">
15 pages, 8892 KiB  
Article
Chlorophyll Fluorescence and Fruit Quality Response of Blueberry to Different Mulches
by Jorge Retamal-Salgado, Beder Loor, Juan Hirzel, María Dolores López, Pablo Undurraga, Nelson Zapata, Rosa Vergara-Retamales and Héctor Olivares-Soto
Agronomy 2022, 12(7), 1702; https://doi.org/10.3390/agronomy12071702 - 19 Jul 2022
Cited by 6 | Viewed by 2001
Abstract
Mulch is widely used in blueberry cultivation for weed control; however, there is still uncertainty as to how the use of different types of mulch alters leaf photosynthetic behavior and the quality and productivity of blueberry fruit. The objective of our research was [...] Read more.
Mulch is widely used in blueberry cultivation for weed control; however, there is still uncertainty as to how the use of different types of mulch alters leaf photosynthetic behavior and the quality and productivity of blueberry fruit. The objective of our research was to evaluate the effect of different types of mulch on the physiological, quality and yield characteristics of blueberries. Three treatments were established: T1 (control), T2 (pine bark) and T3 (geotextile) in two cultivars: Ochlockonee and Legacy. The parameters measured were: the photochemical quantum yield of photosystem II (YII), the maximum photochemical efficiency of photosystem II (Fv/Fm), electron transport rate (ETR), fruit quality and yield parameters. The results show lower soil temperature in T1 during the morning (p < 0.05) compared to the two mulch treatments, which was the opposite during the afternoon, the temperatures were more stable and closer to the optimum (21 °C) in T2 and T3, with mulch favoring root and foliar development. On the other hand, the treatments with mulch favored a higher photosynthetic efficiency of photosystem II (YII) at the end of afternoon and were associated with an increased firmness of the fruit; the firmness of all fruits was higher than that in the control treatment (p < 0.05) in the Legacy cultivar, but without differences between them, with values of 73 and 75 gf mm−1 for T2 and T3, respectively, and 67 gf mm−1 for the Control. In addition, it was observed that the use of mulch only increased the fruit yield in the Legacy cultivar, both in T2 and T3, with both being superior to T1 (p < 0.05). It can be concluded that the use of mulch decreases soil temperature in the midday and late afternoon, improving the edaphoclimatic conditions during the development of the blueberry. In addition, plants with mulch have lower stomatal conductance, which promotes greater photosynthetic efficiency during the day, increasing both firmness and fruit yield. Full article
(This article belongs to the Special Issue Agroecology and Organic Horticulture)
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<p>Effects of the different types of mulch: (<b>A</b>) soil temperature (°C); (<b>B</b>) leaf temperature (°C), at three times of the day. For each different the time of day, lowercase letters indicate significant differences for different types of mulch, according to the Tukey test (<span class="html-italic">p</span> &lt; 0.05). The vertical bars correspond to ±the standard error of the mean (<span class="html-italic">n</span> = 9).</p> Full article ">Figure 2
<p>Correlation of stomatal conductance (gs) in different blueberries cultivars grown with different mulches, (<b>A</b>) temperature of leaves (°C), (<b>B</b>) soil temperature, (<b>C</b>) photochemical quantum yield of photosystem II (Y<sub>II</sub>) and (<b>D</b>) firmness of fruit and temperature of leaves. ** Significant at <span class="html-italic">p</span> &lt; 0.001.</p> Full article ">Figure 3
<p>Effects of the different types of mulch on the stomatal conductance in leaves (mmol m<sup>−2</sup> s<sup>−1</sup>) at three times of the day. For each different the time of day, lowercase letters indicate significant differences for different types of mulch, according to the Tukey test (<span class="html-italic">p</span> &lt; 0.05). The vertical bars correspond to ±the standard error of the mean (<span class="html-italic">n</span> = 9).</p> Full article ">Figure 4
<p>Effects of the different types of mulch on the chlorophyll index in leaves (SPAD units) at three times of the day. For each cultivar, different lowercase letters indicate significant differences for different types of mulch, according to the Tukey test (<span class="html-italic">p</span> &lt; 0.05). The vertical bars correspond to ±the standard error of the mean (<span class="html-italic">n</span> = 9).</p> Full article ">Figure 5
<p>Effects of the different types of mulch on the leaf area index. For each cultivar, different lowercase letters indicate significant differences for different types of mulch, according to the Tukey test (<span class="html-italic">p</span> &lt; 0.05). The vertical bars correspond to ±the standard error of the mean (<span class="html-italic">n</span> = 9).</p> Full article ">Figure 6
<p>Effects of the different types of mulch on the equatorial diameter of the fruit. For each cultivar, different lowercase letters indicate significant differences for different types of mulch, according to the Tukey test (<span class="html-italic">p</span> &lt; 0.05). The vertical bars correspond to ±the standard error of the mean (<span class="html-italic">n</span> = 9).</p> Full article ">Figure 7
<p>Effects of the different types of mulch on the firmness of the fruit. For each cultivar, different lowercase letters indicate significant differences for different types of mulch, according to the Tukey test (<span class="html-italic">p</span> &lt; 0.05). The vertical bars correspond to ±the standard error of the mean (<span class="html-italic">n</span> = 9).</p> Full article ">Figure 8
<p>Effects of the different types of mulch on the yield per plant (kg plant<sup>−1</sup>). For each cultivar, different lowercase letters indicate significant differences for different types of mulch, according to the Tukey test (<span class="html-italic">p</span> &lt; 0.05). The vertical bars correspond to ±the standard error of the mean (<span class="html-italic">n</span> = 9).</p> Full article ">
17 pages, 2626 KiB  
Article
Kānuka Trees Facilitate Pasture Production Increases in New Zealand Hill Country
by Thomas H. Mackay-Smith, Ignacio F. López, Lucy L. Burkitt and Janet I. Reid
Agronomy 2022, 12(7), 1701; https://doi.org/10.3390/agronomy12071701 - 18 Jul 2022
Cited by 3 | Viewed by 2524
Abstract
‘Tree-pasture’ silvopastoral systems have the potential to become transformative multifunctional landscapes that add both environmental and economic value to pastoral farms. Nevertheless, no published study has found increased pasture production under mature silvopastoral trees in New Zealand hill country. This study takes a [...] Read more.
‘Tree-pasture’ silvopastoral systems have the potential to become transformative multifunctional landscapes that add both environmental and economic value to pastoral farms. Nevertheless, no published study has found increased pasture production under mature silvopastoral trees in New Zealand hill country. This study takes a novel approach to silvopastoral research in New Zealand, and investigates a genus that has similar bio-physical attributes to other global silvopastoral trees that have been shown to increase pasture production under their canopies, with the aim of finding a silvopastoral genera that can increase pasture production under tree canopies compared to open pasture in New Zealand. This study measures pasture and soil variables in two pasture positions: under individually spaced native kānuka (Kunzea spp.) trees (kānuka pasture) and paired open pasture positions at least 15 m from tree trunks (open pasture) at two sites over two years. There was 107.9% more pasture production in kānuka pasture positions. The soil variables that were significantly greater in kānuka pasture were Olsen-P (+115.7%, p < 0.001), K (+100%, p < 0.001), Mg (+33.33%, p < 0.01), Na (+200%, p < 0.001) and porosity (+8.8%, p < 0.05), and Olsen-P, porosity and K best explained the variation between kānuka pasture and open pasture positions. Volumetric soil moisture was statistically similar in kānuka pasture and open pasture positions. These results are evidence of nutrient transfer by livestock to the tree-pasture environment. Furthermore, as there was a significantly greater porosity and 48.6% more organic matter under the trees, there were likely other processes also contributing to the difference between tree and open pasture environments, such as litterfall. These results show that kānuka has potential to increase pasture production in New Zealand hill country farms and create multifunctional landscapes enhancing both production and environmental outcomes in pastoral farms. Full article
(This article belongs to the Special Issue Silvopastoralism and Agroforestry for Forage Production)
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<p>Study site locations and individual trees evaluated at each site. (<b>A</b>): Location of the study sites in New Zealand (the red dot is the Wairarapa site and the yellow dot is the Hawkes Bay site). (<b>B</b>): The studied kānuka trees at the Wairarapa site (red dots show the individual trees evaluated). (<b>C</b>): The studied kānuka trees at the Hawkes Bay site (red dots show the individual trees evaluated). (<b>B</b>,<b>C</b>) are from the same satellite layer as (<b>A</b>).</p> Full article ">Figure 2
<p>Pasture production and abiotic factor treatment and site interactions. The error bars are the 95% confidence intervals. ns = not significant. Wa = Wairarapa. Hb = Hawkes Bay. GDMP = green dry matter production. DM = dead matter. G/D = green:dead. P = phosphorus. S = sulphur. N = Nitrogen. CEC = cation exchange capacity. K = potassium. Ca = calcium. Mg = magnesium. Na = sodium. Porosity = porosity 2–5 cm. AWC = available water content 2–5 cm. Macroporosity = macroporosity 2–5 cm. BD = bulk density. VSM = volumetric soil moisture. Su = summer.</p> Full article ">Figure 3
<p>Canonical variate analysis showing which variables best explain treatment and site differences. GDMP = Green dry matter production. P = phosphorus. S = Sulfur. K = potassium. CEC = cation exchange capacity. VSM = volumetric soil moisture.</p> Full article ">
17 pages, 2209 KiB  
Article
Optimized Phosphorus Application Alleviated Adverse Effects of Short-Term Low-Temperature Stress in Winter Wheat by Enhancing Photosynthesis and Improved Accumulation and Partitioning of Dry Matter
by Hui Xu, Zhaochen Wu, Bo Xu, Dongyue Sun, Muhammad Ahmad Hassan, Hongmei Cai, Yu Wu, Min Yu, Anheng Chen, Jincai Li and Xiang Chen
Agronomy 2022, 12(7), 1700; https://doi.org/10.3390/agronomy12071700 - 18 Jul 2022
Cited by 13 | Viewed by 2567
Abstract
Low-temperature stress has become an important abiotic factor affecting high and stable wheat production. Therefore, it is necessary to take appropriate measures to enhance low-temperature tolerance in wheat. A pot experiment was carried out using Yannong19 (YN19, a cold-tolerant cultivar) and Xinmai26 (XM26, [...] Read more.
Low-temperature stress has become an important abiotic factor affecting high and stable wheat production. Therefore, it is necessary to take appropriate measures to enhance low-temperature tolerance in wheat. A pot experiment was carried out using Yannong19 (YN19, a cold-tolerant cultivar) and Xinmai26 (XM26, a cold-sensitive cultivar). We employed traditional phosphorus application (TPA, i.e., R1) and optimized phosphorus application (OPA, i.e., R2) methods. Plants undertook chilling (T1 at 4 °C) and freezing treatment (T2 at −4 °C) as well as ambient temperature (CK at 11 °C) during the anther differentiation period to investigate the effects of OPA and TPA on photosynthetic parameters and the accumulation and distribution of dry matter. The net photosynthetic rate (Pn), stomatal conductance (Gs) and transpiration rate (Tr) of flag leaves decreased in low-temperature treatments, whereas intercellular carbon dioxide concentration (Ci) increased. Compared with R1CK, Pn in R1T1 and R1T2 treatments was reduced by 26.8% and 42.2% in YN19 and 34.2% and 54.7% in XM26, respectively. In contrast, it increased by 6.5%, 8.9% and 12.7% in YN19 and 7.7%, 15.6% and 22.6% in XM26 for R2CK, R2T1 and R2T2 treatments, respectively, under OPA compared with TPA at the same temperature treatments. Moreover, low-temperature stress reduced dry matter accumulation at the reproductive growth stage. OPA increased dry matter accumulation of vegetative organs after the flowering stage and promoted the transportation of assimilates to grains. Hence, the grain number per spike (GNPS), 1000-grain weight (TGW) and yield per plant (YPP) increased. The low-temperature treatments of T1 and T2 caused yield losses of 24.1~64.1%, and the yield increased by 8.6~20.5% under OPA treatments among the two wheat cultivars. In brief, OPA enhances low-temperature tolerance in wheat, effectively improves wheat architecture and photosynthesis, increases GNPS and TGW and ultimately lessens yield losses. Full article
(This article belongs to the Special Issue Strategizing Agricultural Management for Climate Change Adaptation and Mitigation-Series II)
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<p>The weather conditions during March at anther differentiation period in the wheat-growing season (2020−2021).</p> Full article ">Figure 2
<p>Effects of OPA on wheat morphology under low-temperature stress at booting, flowering and grain-filling stages. (<b>A</b>) Plant height of YN19. (<b>B</b>) Plant height of XM26. (<b>C</b>) Tiller number of YN19. (<b>D</b>) Tiller number of XM26. (<b>E</b>) Leaf areas per plant of YN19. (<b>F</b>) Leaf areas per plant of XM26. Data represent means ± SE (<span class="html-italic">n =</span> 3). Vertical bars represent standard errors. Abbreviations: YN19 = Yannong19; XM26 = Xinmai26; BS = booting stage; FS = flowering stage; GS = grain-filling stage; MS = maturity stage. R1CK, R1T1 and R1T2 represent 11 °C, 4 °C and −4 °C under traditional phosphorus application, respectively; and R2CK, R2T1 and R2T2 represent 11 °C, 4 °C and −4 °C under optimized phosphorus application, respectively.</p> Full article ">Figure 3
<p>Effects of OPA on dry matter accumulation in different wheat organs under low-temperature stress at different growth stages. (<b>A</b>) Different organs dry matter accumulation of Yannong19 at the flowering stage. (<b>B</b>) Different organs dry matter accumulation of Xinmai26 at the flowering stage. (<b>C</b>) Different organs dry matter accumulation of Yannong19 at the maturity stage. (<b>D</b>) Different organs dry matter accumulation of Xinmai26 at the maturity stage. Data represent mean ± SE (<span class="html-italic">n =</span> 3). Different letters following the data within each column indicate significant differences at <span class="html-italic">p &lt;</span> 0.05. Aboveground vegetative organs: leaves + stems + sheaths. R1 and R2 represent traditional phosphorus application and optimized phosphorus application, respectively. CK, T1, and T2 represent 11 °C, 4 °C and −4 °C, respectively.</p> Full article ">Figure 4
<p>Correlation analysis of morphological traits, photosynthesis, dry matter assimilation and transportation with yield. Abbreviations: PH: plant height at the grain-filling stage; LA: leaf area at the grain-filling stage; Pn: the net photosynthetic rate at the grain-filling stage; Gs: stomatal conductance at the grain-filling stage; Ci: intercellular carbon-dioxide concentration at the grain-filling stage; Tr: transpiration rate at the grain-filling stage; DA: dry matter accumulation in maturity stage; DMAA: dry matter accumulation of vegetative organs after flowering stage; SNPP: spike number per plant; GNPS: grain number per spike; TGW: 1000-grain weight; YPP: yield per plant.</p> Full article ">
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