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Agronomy, Volume 9, Issue 2 (February 2019) – 69 articles

Cover Story (view full-size image): Our study found that rice husk biochar addition on upland field converted paddy could greatly improve growth and yield of sesame as well as seed mineral contents. The study showed that the higher rate of biochar addition significantly improved the seed yield of sesame in the first cropping whereas in the second cropping field, biochar addition did not significantly influence seed yield. The overall improvement in the sesame growth, seed yield and mineral contents especially K was attributed to mainly increased K availability, soil pH, CEC, improved porosity and bulk density. Our study also highlighted that rice husk biochar addition may not have a long lasting effect on sesame yield on upland field converted paddy since the positive effect of biochar tended to fade in the second cropping suggesting one time application would not be sufficient. View this paper
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15 pages, 581 KiB  
Article
Effects of Temperature and Grafting on Yield, Nutrient Uptake, and Water Use Efficiency of a Hydroponic Sweet Pepper Crop
by Andreas Ropokis, Georgia Ntatsi, Constantinos Kittas, Nikolaos Katsoulas and Dimitrios Savvas
Agronomy 2019, 9(2), 110; https://doi.org/10.3390/agronomy9020110 - 25 Feb 2019
Cited by 32 | Viewed by 6740
Abstract
In areas characterized by mild winter climate, pepper is frequently cultivated in unheated greenhouses in which the temperature during the winter may drop to suboptimal levels. Under low temperature (LT) conditions, the uptake of nutrients may be altered in a different manner than [...] Read more.
In areas characterized by mild winter climate, pepper is frequently cultivated in unheated greenhouses in which the temperature during the winter may drop to suboptimal levels. Under low temperature (LT) conditions, the uptake of nutrients may be altered in a different manner than that of the water and thus their uptake ratio, known as uptake concentration, may be different than in greenhouses with standard temperature (ST) conditions. In the present study, pepper plants of the cultivars “Sammy” and “Orangery”, self-grafted or grafted onto two commercial rootstocks (“Robusto” and “Terrano”), were cultivated in a greenhouse under either ST or LT temperature conditions. The aim of the study was to test the impact of grafting and greenhouse temperature on total yield, water use efficiency, and nutrient uptake. The LT regime reduced the yield by about 50% in “Sammy” and 33% in “Orangery”, irrespective of the grafting combination. Grafting of “Sammy” onto both “Robusto” and “Terrano” increased the total fruit yield by 39% and 34% compared with the self-grafted control, while grafting of “Orangery” increased the yield only when the rootstock was “Terrano”. The yield increase resulted exclusively from enhancement of the fruit number per plant. Both the water consumption and the water use efficiency were negatively affected by the LT regime, however the temperature effect interacted with the rootstock/scion combination. The LT increased the uptake concentrations (UC) of K, Ca, Mg, N, and Mn, while it decreased strongly that of P and slightly the UC of Fe and Zn. The UC of K and Mg were influenced by the rootstock/scion combination, however this effect interacted with the temperature regime. In contrast, the Ca, N, and P concentrations were not influenced by the grafting combination. The results of the present study show that the impact of grafting on yield and nutrient uptake in pepper depend not merely on the rootstock genotype, however on the rootstock/scion combination. Full article
(This article belongs to the Collection Nutrition Management of Hydroponic Vegetable Crops)
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<p>Fluctuations of (<b>a</b>) mean temperature (°C) and (<b>b</b>) mean relative humidity (%) during 790 degree-days from treatment initiation in two greenhouse compartments in which a standard temperature (ST) and a low temperature (LT) regime were maintained during the experimental period (from 29 November to 20 May).</p>
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19 pages, 3516 KiB  
Article
Optimizing the Sowing Date and Irrigation Strategy to Improve Maize Yield by Using CERES (Crop Estimation through Resource and Environment Synthesis)-Maize Model
by Qaisar Saddique, Huanjie Cai, Wajid Ishaque, Hui Chen, Henry Wai Chau, Muhammad Umer Chattha, Muhammad Umair Hassan, Muhammad Imran Khan and Jianqiang He
Agronomy 2019, 9(2), 109; https://doi.org/10.3390/agronomy9020109 - 25 Feb 2019
Cited by 26 | Viewed by 5306
Abstract
Summer maize (Zea mays L.) is a widely cultivated crop in the arid and semi-arid Guanzhong region of China. However, due to the spatial and temporal variation in rainfall, the seasonal maize yield varies substantially and occasionally is not economical for poor [...] Read more.
Summer maize (Zea mays L.) is a widely cultivated crop in the arid and semi-arid Guanzhong region of China. However, due to the spatial and temporal variation in rainfall, the seasonal maize yield varies substantially and occasionally is not economical for poor farmers to produce. Recent water-saving agricultural practices were developed by the government to make it possible to apply supplementary irrigation at optimum sowing dates to maximize maize production under limited rainfall in the region. CERES (Crop Estimation through Resource and Environment Synthesis)-maize model was used to identify the appropriate irrigation strategies, crop growth stages and sowing dates for sustainable maize production. Model calibration process were carried out for full irrigation treatments of four growing seasons, (2012–2015). The data used for calibration included: Crop phenology, grain yield, aboveground biomass and leaf area index. The calibration phase model showed good agreement between simulated and observed values, with normalized root mean square error (nRMSE) ranging from 4.51% to 14.5%. The performance of the calibrated model was evaluated using the field data of grain yield, aboveground biomass, leaf area index and water use efficiency. The performance of the model during evaluation was satisfactory with acceptable nRMSE error ranging from 7% to 10%. Soil moisture content was evaluated for full irrigation treatments for both 2012 and 2013 seasons. With results showing that soil moisture content below 35 cm layer was well simulated with nRMSE, 0.57 to 0.86 respectively. Appropriate simulated sowing dates for higher production and water productivity were from 14 to 24 June. The proper amount and timing of irrigation water application was 100 mm at the flowering stage, and 100 mm at the grain filling stage respectively. Summer maize yield can be improved by adjusting the sowing date and applying supplementary irrigation when precipitation cannot meet the crop water demand in the Guanzhong Plain. Full article
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<p>Map location of Yangling field experiment &amp; Wugong weather station in China.</p>
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<p>(<b>a</b>) Average monthly maximum, minimum temperature (<sup>o</sup>C) and rainfall (mm) during maize growing season (2012–2015) (<b>b</b>) Historical weather data (1961–2011) for Yangling, China.</p>
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<p>(<b>a</b>–<b>b</b>) Field layout with and without rain-out shelter, (<b>c</b>) Lysimeter data recorder computer (<b>d</b>) Lysimeter installed structure</p>
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<p>(<b>a</b>–<b>b</b>) Field layout with and without rain-out shelter, (<b>c</b>) Lysimeter data recorder computer (<b>d</b>) Lysimeter installed structure</p>
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<p>Comparison between simulated and observed (<b>a</b>) grain yield (<b>b</b>) aboveground biomass under different irrigation treatments for 2012 and 2013 growing season.</p>
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<p>Simulated and observed soil moisture content at different soil depth for (<b>a</b>–<b>c</b>) 2012 and (<b>a</b>–<b>c</b>) 2013 growing season. DAS: Days After Sowing.</p>
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<p>Simulated and observed leaf area index growing season (<b>a</b>–<b>c</b>) 2012 and (<b>a</b>–<b>c</b>) 2013. Horizontal bars are standard deviations DAS: Days After Sowing.</p>
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<p>Simulated maize yield under different irrigation scenario (I1–I12) using historical weather data with different sowing dates.</p>
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<p>Summer maize crop growing period Categorized based on the precipitation quantity using the historical weather data.</p>
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<p>Simulated maize yield different irrigation scenario (I1–I12) based on the precipitation quantity using the historical weather data.</p>
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<p>Simulated water use efficiency of different irrigation scenario (I1–I12) based on the precipitation quantity using the historical weather data.</p>
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17 pages, 6769 KiB  
Article
Using Neural Networks to Estimate Site-Specific Crop Evapotranspiration with Low-Cost Sensors
by Jason Kelley and Eric R. Pardyjak
Agronomy 2019, 9(2), 108; https://doi.org/10.3390/agronomy9020108 - 23 Feb 2019
Cited by 27 | Viewed by 4809
Abstract
Irrigation efficiency is facilitated by matching irrigation rates to crop water demand based on estimates of actual evapotranspiration (ET). In production settings, monitoring of water demand is typically accomplished by measuring reference ET rather than actual ET, which is then adjusted approximately using [...] Read more.
Irrigation efficiency is facilitated by matching irrigation rates to crop water demand based on estimates of actual evapotranspiration (ET). In production settings, monitoring of water demand is typically accomplished by measuring reference ET rather than actual ET, which is then adjusted approximately using simplified crop coefficients based on calendars of crop maturation. Methods to determine actual ET are usually limited to use in research experiments for reasons of cost, labor and requisite user skill. To pair monitoring and research methods, we co-located eddy covariance sensors with on-farm weather stations over two different irrigated crops (vegetable beans and hazelnuts). Neural networks were used to train a neural network and utilize on-farm weather sensors to report actual ET as measured by the eddy covariance method. This approach was able to robustly estimate ET from as few as four sensor parameters (temperature, solar radiation, humidity and wind speed) with training time as brief as one week. An important limitation found with this machine learning method is that the trained network is only valid under environmental and crop conditions similar to the training period. The small number of required sensors and short training times demonstrate that this approach can estimate site-specific and crop specific ET. With additional field validation, this approach may offer a new method to monitor actual crop water demand for irrigation management. Full article
(This article belongs to the Special Issue Crop Evapotranspiration)
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<p>Typical sensor array used in the field experiments shown at site 1. Crop (green beans) and center pivot irrigation system is visible in the background. Note that this is not the full array used for the 8-month period at site 1 but an auxiliary system that is shown for clarity. The eddy-covariance system used here (IRGASON) is visible extending to the right.</p>
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<p>Photograph of experimental site 2 (Hazelnut orchard) showing the eddy-covariance system, net radiometer and other sensors on a 10-m tower in background.</p>
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<p>Excerpted time series of latent heat flux determined by the eddy-covariance measurements, Penman-Monteith equation (with no crop coefficient applied) and the actual latent heat flux estimated by the artificial neural network (ANN) method. The shaded area indicates the period used for training the ANN.</p>
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<p>Results at site 1 from (<b>a</b>) 3-day training and (<b>b</b>) 14 day training period. The RMSE indicates the departure of the dependent values (P-M and ANN) from the eddy covariance control.</p>
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<p>Method comparisons at site 1 from 7 day training (<b>a</b>) mid-season, when the crop was adequately irrigated; and (<b>b</b>) at the end of season, when irrigation was over-applied.</p>
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<p>Training results at site 1 using 7-day training periods—Trained during (<b>a</b>) midseason and (<b>b</b>) end of season. Although half hour comparisons for the two trainings do not appear significantly different (<a href="#agronomy-09-00108-f005" class="html-fig">Figure 5</a>), the cumulative water use plot shows that training during a period of over watering (<b>b</b>) results in the ANN estimating significantly greater ET over the entire two month growing season.</p>
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<p>Time series of latent flux at Site 1. The variability in ETa followed the 4 to 5-day irrigation cycle (irrigation occurring on 2 July, 8 July, 12 July, 17 July).</p>
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<p>Average of ratio of 30-min ET<sub>a</sub>:ET<sub>ref</sub>, grouped by the number of days since last irrigation (at site 1). Boxes indicate the 1st to 3rd quartile range (25th–75th percentile), with the median value indicated by the red line. Whiskers bound the most extreme values within ± 2.7σ, with outliers outside these bounds indicated by “+” markers.</p>
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<p>Method comparison for ET at site 2 (hazelnut orchard) resulting from two week training period.</p>
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<p>Cumulative ET and water use observed at site 2 (hazelnut orchard). The two week training period is shaded in blue.</p>
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20 pages, 1904 KiB  
Article
Optimizing Crop Load for New Apple Cultivar: “WA38”
by Brendon Anthony, Sara Serra and Stefano Musacchi
Agronomy 2019, 9(2), 107; https://doi.org/10.3390/agronomy9020107 - 22 Feb 2019
Cited by 33 | Viewed by 5993
Abstract
Crop load management is growing increasingly important as a factor related to biennial tendencies, post-harvest disorders, and inconsistent fruit quality in apples like “Honeycrisp”. Washington State University released a new apple cultivar, called “WA38”, in 2017. Limited literature is available about the productive [...] Read more.
Crop load management is growing increasingly important as a factor related to biennial tendencies, post-harvest disorders, and inconsistent fruit quality in apples like “Honeycrisp”. Washington State University released a new apple cultivar, called “WA38”, in 2017. Limited literature is available about the productive characteristics of this new cultivar. An experimental trial evaluating the effect of crop load on leaf area, fruit quality, mineral composition, and return bloom of “WA38” was conducted for two consecutive years (2017 and 2018) to determine an optimal crop load. Trees were trained as a spindle and grafted on Malling-9 Nic29 (Nic29) rootstocks. Crop loads were adjusted to 2, 4, 6, and 8 fruits/cm2 of trunk cross-sectional area (TCSA). Crop load had a significant effect on production, with yields ranging from 28 to 83 MT/ha in 2017. Fruit quality was impacted by increasing crop load, with a reduction in fruit weight, soluble solid content, firmness, dry matter, titratable acidity, and a delay in maturity. Leaf-to-fruit ratios were higher in lower crop loads. Relatively consistent flower bud formation was seen at the 6 and 8 fruits/cm2 categories. A possible threshold for optimal fruit quality and consistent bloom was identified around 6 fruits/cm2 TCSA. Full article
(This article belongs to the Section Horticultural and Floricultural Crops)
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<p>Distribution (% fruit) of fruit size (mm) by each crop load (2.1, 4.1, 6.0, 7.8 fruits/cm<sup>2</sup>) level at harvest for “WA38” apples grown in Washington State, Wenatchee area in 2017.</p>
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<p>Distribution (% fruit) of fruit size (mm) by each crop load (2.1, 4.1, 6.0, 7.8 fruits/cm<sup>2</sup>) level induced in 2017 at harvest for “WA38” apples grown in Washington State, Wenatchee area in 2018. Crop loads were manually induced in 2017, but the trees were left to bear naturally in 2018.</p>
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<p>Distribution (% fruit) of index of absorbance difference (I<sub>AD</sub>) measurements by DA meter [<a href="#B35-agronomy-09-00107" class="html-bibr">35</a>] for “WA38” apples grown in the different crop load (2.1, 4.1, 6.0, 7.8 fruits/cm<sup>2</sup>) levels observed immediately after harvest (no storage) in Washington State, Wenatchee area in 2017.</p>
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<p>Fruit class rankings (extra fancy, fancy, and cull) for “WA38” fruit harvested in the fall of 2017 in Washington State, Wenatchee area, across each crop load (2.1, 4.1, 6.0, 7.8 fruits/cm<sup>2</sup>) category. All fruits per trial tree were rated immediately after harvest on 9 October 2017.</p>
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<p>Fruit class rankings (fancy and cull) for “WA38” fruit harvested in the fall of 2018 in Washington State, Wenatchee area, across each crop load (2.1, 4.1, 6.0, 7.8 fruits/cm<sup>2</sup>) category that was set in 2017. All fruits per trial tree were rated immediately after harvest on 21 September 2018. Fancy and extra fancy rankings were combined due to a minimal number of extra fancy fruit. Crop loads were not induced in 2018; this was the natural bearing tendency after crop load was induced in 2017.</p>
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17 pages, 2053 KiB  
Article
Development of Chlorophyll-Meter-Index-Based Dynamic Models for Evaluation of High-Yield Japonica Rice Production in Yangtze River Reaches
by Ke Zhang, Xiaojun Liu, Syed Tahir Ata-Ul-Karim, Jingshan Lu, Brian Krienke, Songyang Li, Qiang Cao, Yan Zhu, Weixing Cao and Yongchao Tian
Agronomy 2019, 9(2), 106; https://doi.org/10.3390/agronomy9020106 - 22 Feb 2019
Cited by 16 | Viewed by 4645
Abstract
Accurate estimation of the nitrogen (N) spatial distribution of rice (Oryza sativa L.) is imperative when it is sought to maintain regional and global carbon balances. We systematically evaluated the normalized differences of the soil and plant analysis development (SPAD) index (the [...] Read more.
Accurate estimation of the nitrogen (N) spatial distribution of rice (Oryza sativa L.) is imperative when it is sought to maintain regional and global carbon balances. We systematically evaluated the normalized differences of the soil and plant analysis development (SPAD) index (the normalized difference SPAD indexes, NDSIs) between the upper (the first and second leaves from the top), and lower (the third and fourth leaves from the top) leaves of Japonica rice. Four multi-location, multi-N rate (0–390 kg ha−1) field experiments were conducted using seven Japonica rice cultivars (9915, 27123, Wuxiangjing14, Wunyunjing19, Wunyunjing24, Liangyou9, and Yongyou8). Growth analyses were performed at different growth stages ranging from tillering (TI) to the ripening period (RP). We measured leaf N concentration (LNC), the N nutrition index (NNI), the NDSI, and rice grain yield at maturity. The relationships among the NDSI, LNC, and NNI at different growth stages showed that the NDSI values of the third and fourth fully expanded leaves more reliably reflected the N nutritional status than those of the first and second fully expanded leaves (LNC: NDSIL3,4, R2 > 0.81; NDSIothers, 0.77 > R2 > 0.06; NNI: NDSIL3,4, R2 > 0.83; NDSIothers, 0.76 > R2 > 0.07; all p < 0.01). Two new diagnostic models based on the NDSIL3,4 (from the tillering to the ripening period) can be used for effective diagnosis of the LNC and NNI, which exhibited reasonable distributions of residuals (LNC: relative root mean square error (RRMSE) = 0.0683; NNI: RRMSE = 0.0688; p < 0.01). The relationship between grain yield, predicted yield, and NDSIL3,4 were established during critical growth stages (from the stem elongation to the heading stages; R2 = 0.53, p < 0.01, RRMSE = 0.106). An NDSIL3,4 high-yield change curve was drawn to describe critical NDSIL3,4 values for a high-yield target (10.28 t ha−1). Furthermore, dynamic-critical curve models based on the NDSIL3,4 allowed a precise description of rice N status, facilitating the timing of fertilization decisions to optimize yields in the intensive rice cropping systems of eastern China. Full article
(This article belongs to the Special Issue Remote Sensing Applications for Agriculture and Crop Modelling)
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<p>Changes over time in the soil and plant analysis development (SPAD) readings of different leaves (“LFT” represents the fully expanded leaf position form the top; “1-4 LFT” means one to four fully expanded leaf position form the top.; 1LFT, <b>A</b>; 2LFT, <b>B</b>; 3LFT, <b>C</b>; and 4LFT, <b>D</b>) of 27123 plants evaluated in 2007 and 2008 at N levels of 120 kg ha<sup>−1</sup>. The vertical bars are standard error (TI, tillering; SE, stem elongation; PI, panicle initiation; BT, booting; HD, heading; FL, flowering; GF, grain filling; and RP, ripening).</p>
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<p>Regression fits between the leaf nitrogen concentration (LNC, <b>A</b>), nitrogen nutrition index (NNI, <b>B</b>), and NDSI<sub>L3,4</sub>. The experimental years are shown as 2007, 2008, or 2013; 9915, 27123, and WXJ14 are the Wuxiangjing 14, WYJ19 means Wuyunjing19, and YY8 is Yongyou8. “**” means significant difference at 0.01 probability level.</p>
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<p>The relationship between measured and predicted leaf nitrogen concentration (LNC, <b>a</b>), nitrogen nutrition index (NNI, <b>b</b>) of four rice cultivars from the time of stem elongation (SE) to heading (HD) (WYJ19, Wuyungjing19; YY8, Yongyou8; WXJ14, Wuxiangjing14; and WYJ24, Wuyungjing 24; Japonica). The solid line is the linear regression line and the dotted line a line inclined at 45° to the axis. ** indicates significant difference at 0.01 probability level.</p>
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<p>Correlations between NDSI<sub>L3,4</sub> values and grain yields at critical growth stages (from stem elongation to heading; cultivars: 9915, 27123 (2007); wuxiangjing14, 27123; 2013: wuyunjing19, wuxiangjing19 (2008)).</p>
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<p>Relationships between measured and predicted grain yields of four rice cultivars in 2009 (WYJ19, Wuyungjing19; YY8, Yongyou8; WXJ14, Wuxiangjing14; and WYJ24, Wuyungjing24; Japonica).‘<span class="html-italic">x</span>’ represents measured values, ‘<span class="html-italic">y</span>’ means predicted values. The solid line is the linear regression line and the dotted line a line inclined at 45° to the axis.</p>
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<p>Critical NDSI<sub>L3,4</sub> data points used to define the changes in the NDSI<sub>L3,4</sub> curve when data of high-yield targets were pooled. The solid line is the critical NDSI<sub>L3,4</sub> change curve (<math display="inline"><semantics> <mrow> <mi>N</mi> <mi>D</mi> <mi>S</mi> <msub> <mi>I</mi> <mrow> <mn>3</mn> <mo>,</mo> <mn>4</mn> </mrow> </msub> <mo>=</mo> <mn>0.0195</mn> <mo>+</mo> <mfrac> <mrow> <mo>−</mo> <mn>0.0158</mn> <mo>−</mo> <mn>0.0195</mn> </mrow> <mrow> <mn>1</mn> <mo>+</mo> <msup> <mi>e</mi> <mrow> <mfrac> <mrow> <mi>D</mi> <mi>O</mi> <mi>Y</mi> <mo>−</mo> <mn>235.91</mn> </mrow> <mrow> <mn>6.95</mn> </mrow> </mfrac> </mrow> </msup> </mrow> </mfrac> </mrow> </semantics></math>; the A<sub>1</sub>, A<sub>2</sub>, <span class="html-italic">x</span><sub>0</sub>, and dx values differ in terms of their effect on NDSI<sub>L3,4</sub> trends) of high-yield target rice in the Yangtze river valley.</p>
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23 pages, 2475 KiB  
Article
Microbial Consortia versus Single-Strain Inoculants: An Advantage in PGPM-Assisted Tomato Production?
by Klára Bradáčová, Andrea S. Florea, Asher Bar-Tal, Dror Minz, Uri Yermiyahu, Raneen Shawahna, Judith Kraut-Cohen, Avihai Zolti, Ran Erel, K. Dietel, Markus Weinmann, Beate Zimmermann, Nils Berger, Uwe Ludewig, Guenter Neumann and Gheorghe Poşta
Agronomy 2019, 9(2), 105; https://doi.org/10.3390/agronomy9020105 - 22 Feb 2019
Cited by 100 | Viewed by 9239
Abstract
The use of biostimulants with plant growth-promoting properties, but without significant input of nutrients, is discussed as a strategy to increase stress resistance and nutrient use efficiency of crops. However, limited reproducibility under real production conditions remains a major challenge. The use of [...] Read more.
The use of biostimulants with plant growth-promoting properties, but without significant input of nutrients, is discussed as a strategy to increase stress resistance and nutrient use efficiency of crops. However, limited reproducibility under real production conditions remains a major challenge. The use of combination products based on microbial and non-microbial biostimulants or microbial consortia, with the aim to exploit complementary or synergistic interactions and increase the flexibility of responses under different environmental conditions, is discussed as a potential strategy to overcome this problem. This study aimed at comparing the efficiency of selected microbial single-strain inoculants with proven plant-growth promoting potential versus consortium products under real production conditions in large-scale tomato cultivation systems, exposed to different environmental challenges. In a protected greenhouse production system at Timisoara, Romania, with composted cow manure, guano, hair-, and feather-meals as major fertilizers, different fungal and bacterial single-strain inoculants, as well as microbial consortium products, showed very similar beneficial responses. Nursery performance, fruit setting, fruit size distribution, seasonal yield share, and cumulative yield (39–84% as compared to the control) were significantly improved over two growing periods. By contrast, superior performance of the microbial consortia products (MCPs) was recorded under more challenging environmental conditions in an open-field drip-fertigated tomato production system in the Negev desert, Israel with mineral fertilization on a high pH (7.9), low fertility, and sandy soil. This was reflected by improved phosphate (P) acquisition, a stimulation of vegetative shoot biomass production and increased final fruit yield under conditions of limited P supply. Moreover, MCP inoculation was associated with selective changes of the rhizosphere-bacterial community structure particularly with respect to Sphingobacteriia and Flavobacteria, reported as salinity indicators and drought stress protectants. Phosphate limitation reduced the diversity of bacterial populations at the root surface (rhizoplane) and this effect was reverted by MCP inoculation, reflecting the improved P status of the plants. The results support the hypothesis that the use of microbial consortia can increase the efficiency and reproducibility of BS-assisted strategies for crop production, particularly under challenging environmental conditions. Full article
(This article belongs to the Special Issue Plant Mineral Nutrition: Principles and Perspectives)
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<p>Leaf area (<b>A</b>,<b>B</b>) and plant height (<b>C</b>,<b>D</b>) of tomato cv Primadona F1 during the nursery phase at 43 days after sowing. Columns represent means ± standard deviation (<span class="html-italic">n</span> = 4 with each 10 plants as subsamples). Significant treatment differences (Tukey test, <span class="html-italic">p</span> ≤ 0.05) are indicated by different characters.</p>
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<p>Cumulative yield (t ha<sup>−1</sup>) and fruit size distribution (t ha<sup>−1</sup> and %) for greenhouse tomato production with different BS treatments in 2016 (<b>A</b>) and 2017 (<b>B</b>). Quality classes (g FW fruit<sup>−1</sup>): extra-large: &gt;200 g, class I: 150–200 g, and class II: &lt;150 g. Columns represent mean values of cumulative fruit yield ± SD (<span class="html-italic">n</span> = 4). Significant treatment differences in cumulative yield (Tukey test, <span class="html-italic">p</span> ≤ 0.05) are indicated by different characters.</p>
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<p>Seasonal yield share of tomato production over the whole harvesting period (June, July, and August) for different BS applications in Romania 2016 (<b>A</b>) and 2017 (<b>B</b>) (fruit yield in t ha<sup>−1</sup>).</p>
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<p>Effects of microbial consortia product (MCP) inoculation without external P fertilization on field performance of tomato plants at four months after sowing in comparison with different levels of P (triple superphosphate) fertilization in a field experiment at Ramat, Negev, Israel.</p>
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<p>Shannon index for mean α-diversity of the bacterial communities in root-affected soil (<b>A</b>) and the rhizoplane (<b>B</b>) of drip-irrigated tomato plants with and without band placement of triple superphosphate (12.5 kg P ha<sup>−1</sup>) and inoculation with different microbial biostimulants at 6 months after sowing, Negev Ramat, Israel. Significant differences (paired Student’s <span class="html-italic">t</span>-test) in Shannon index between 0 and 12.5 kg P ha<sup>−1</sup> dose of the same inoculant treatment are marked by *. Significant differences after pairwise comparison between inoculation treatments with the same P dose are indicated by different characters: A, B for 0 P, and a,b for 12.5 kg P ha<sup>−1</sup>.</p>
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<p>Relative abundance of different bacterial taxa at the rhizoplane (<b>A</b>) and in the root-affected soil (<b>B</b>) of drip-irrigated tomato plants with and without band placement of triple superphosphate (12.5 kg P ha<sup>−1</sup>) and inoculation with different microbial BS at 6 months after sowing, Negev, Ramat, Israel.</p>
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18 pages, 5090 KiB  
Article
Optimization of Offshoot Outgrowth in Globe Artichoke Using a Combination of Chemical and Mechanical Treatments
by Jouhaina Riahi, Carlo Nicoletto, Ghaith Bouzaein, Mohamed Haj Ibrahim, Ismail Ghezal, Paolo Sambo and Karima Kouki Khalfallah
Agronomy 2019, 9(2), 104; https://doi.org/10.3390/agronomy9020104 - 22 Feb 2019
Cited by 4 | Viewed by 3215
Abstract
The application of cytokinins is a good tool to promote axillary buds in many species, but plant decapitation or leaf cut-back are also suitable methods. This research aims to establish a strategy for artichoke cutting production using a combination of chemical and mechanical [...] Read more.
The application of cytokinins is a good tool to promote axillary buds in many species, but plant decapitation or leaf cut-back are also suitable methods. This research aims to establish a strategy for artichoke cutting production using a combination of chemical and mechanical treatments. Two experiments were conducted in Tunisia to investigate the effect of 6-benzylamino purine (BAP) on shoot outgrowth in globe artichoke combined with the leaf cut-back at collar level one week after BAP treatment. The first trial was tested in a spring offshoot nursery and the second one in a field of micro-propagated mother plants grown for two years. Five treatments were tested in both experiments: BAP 0 ppm + no cut-back (T1), BAP 0, 100, 200, and 300 ppm + cut-back (T2, T3, T4, and T5 respectively). Regarding growth aspects, the highest number of offshoots was obtained in T4 for both trials with an increase of 49.2% and 37.8% compared to T2 nursery and field values, respectively. T4 also showed a faster rhythm of shoot emission and the biggest shoot size compared to the other treatments. Significant interactions between BAP treatments and offshoot size were recorded for morphological and weight parameters. Regarding the offshoot mineral composition, relevant differences were observed among BAP treatments; moreover, the higher BAP concentrations induced a significant decrease of NaCl plant uptake. Therefore, the combination of BAP 200 ppm and the leaf cut-back could be a potential method to enlarge the cutting production of globe artichoke also reducing some stressful conditions. Full article
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<p>Monthly averages of maximum and minimum air temperatures and cumulative precipitations registered during the year of the essays (Technical Center of Potato and Artichoke meteorological station, Manouba, Tunisia).</p>
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<p>Experimental design for the trials of nursery offshoots and field artichoke mother plants.</p>
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<p>Number of emitted offshoots from nursery plants according to 6-benzylamino purine (BAP) treatments. T2: BAP 0 ppm + cut-back; T3: BAP 100 ppm + cut-back; T4: BAP 200 ppm + cut-back; T5: BAP 300 ppm + cut-back. The central values represent the arithmetic average and the standard deviation is reported by whiskers. Within the same period different letters indicate significant differences according to Tukey’s HSD Test at <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Chronology of offshoot emission from nursery plants of treatment T4 after cutting back and after 7, 14, 21 days after cut-back (DAC).</p>
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<p>Different effects of 6-benzylamino purine (BAP) treatments on weight traits and dry matter of harvested offshoots from nursery plants according to their size: (<b>A</b>) leaves dry matter, (<b>B</b>) roots fresh weight and (<b>C</b>) roots dry matter. Standard errors are reported. T2: BAP 0 ppm + cut-back; T3: BAP 100 ppm + cut-back; T4: BAP 200 ppm + cut-back; T5: BAP 300 ppm + cut-back; NOW1: nursery offshoot fresh weight &lt; 30 g; NOW2: 30 g ≤ nursery offshoot fresh weight ≤ 60 g; NOW3: nursery offshoot fresh weight &gt; 60 g.</p>
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<p>Number of emitted offshoots from field mother plants according to 6-benzylamino purine (BAP) treatments. Within the same period different letters indicate significant differences according to Tukey’s HSD Test at <span class="html-italic">p</span> &lt; 0.05. Standard errors are reported. T1: BAP 0 ppm + no cut-back; T2: BAP 0 ppm + cut-back; T3: BAP 100 ppm + cut-back; T4: BAP 200 ppm + cut-back; T5: BAP 300 ppm + cut-back. The central values represent the arithmetic average and the standard deviation is reported by whiskers.</p>
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<p>Different effects of 6-benzylamino purine (BAP) treatments on morphological and weight traits of harvested offshoots from field mother plants according to their size: (<b>A</b>) root diameter, (<b>B</b>) root fresh weight, (<b>C</b>) root dry matter, (<b>D</b>) shoot/root ratio. Standard errors are reported. T1: 0 ppm + no cut-back; T2: BAP 0 ppm + cut-back; T3: BAP 100 ppm + cut-back; T4: BAP 200 ppm + cut-back; T5: BAP 300 ppm + cut-back; FOW1: field offshoot weight &lt;100 g; FOW2: 100 g ≤ field offshoot weight ≤150 g; FOW3: field offshoot weight &gt;150 g.</p>
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17 pages, 690 KiB  
Article
Effect of the Intensity of Weed Harrowing with Spike-Tooth Harrow in Barley-Pea Mixture on Yield and Mycobiota of Harvested Grains
by Rafał Ogórek, Agnieszka Lejman and Piotr Sobkowicz
Agronomy 2019, 9(2), 103; https://doi.org/10.3390/agronomy9020103 - 21 Feb 2019
Cited by 8 | Viewed by 3537
Abstract
Harrowing is one of the most popular mechanical methods to control weeds. Nevertheless, the relationship between the effect of different harrowing intensities using spike-tooth harrow in barley-pea intercrop on the yield and mycoflora of grains has not yet been studied. Therefore, the aim [...] Read more.
Harrowing is one of the most popular mechanical methods to control weeds. Nevertheless, the relationship between the effect of different harrowing intensities using spike-tooth harrow in barley-pea intercrop on the yield and mycoflora of grains has not yet been studied. Therefore, the aim of this study was to assess the effect of harrow intensity using spike-tooth harrow in barley-pea mixture on the mycological quality of harvested grains, grain yield, as well as influence of barley and pea grain moisture on the abundance of fungi. The field experiment was carried out during 2010–2012, and it was conducted using a randomized complete block design with four replicates. Weed control was mechanical and chemical. In this study, we have shown that harrowing in barley-pea intercrops does not reduce the yield of either mixture components, both with respect to grain quantity or mycological quality after harvest, compared to controls—without harrowing and the herbicide MCPA (2-methyl-4-chlorophenoxyacetic acid). However, increasing the intensity of harrowing did not result in a consistently larger crop yield or reduction in fungal abundance in the grains. The grains’ internal structures and surface of both tested components of the mixture were colonized to a large extent by cosmopolitan fungi, of which Alternaria alternata (Fr.) Keissl. was the most abundant. Full article
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<p>The percentage each species of microscopic fungi cultured for the disinfected (D) and non-disinfected (ND) grains of spring barely, mean for 2010–2012. <span class="html-italic">Mucor mucedo</span> Fresen. and <span class="html-italic">Penicillium chrysogenum</span> Thom were isolated only from non-disinfected (ND) grains.</p>
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<p>The percentage each species of microscopic fungi cultured for the disinfected (D) and non-disinfected (ND) grains of pea, mean for 2010–2012. <span class="html-italic">Penicillium chrysogenum</span> Thom, <span class="html-italic">Sclerotinia sclerotiorum</span> (Lib.) de Bary and <span class="html-italic">Trichoderma harzianum</span> Rifai were isolated only from non-disinfected (ND) grains.</p>
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13 pages, 908 KiB  
Article
3,4-Dimethylpyrazole Phosphate (DMPP) Reduces N2O Emissions from a Tilled Grassland in the Bogotá Savanna
by Ximena Huérfano, Sergio Menéndez, Matha-Marina Bolaños-Benavides, Carmen González-Murua and José-María Estavillo
Agronomy 2019, 9(2), 102; https://doi.org/10.3390/agronomy9020102 - 21 Feb 2019
Cited by 8 | Viewed by 5500
Abstract
Grasslands are subject to a wide range of land management practices that influence the exchange of the three main agricultural greenhouse gases (GHGs) that are related to agriculture: carbon dioxide (CO2), nitrous oxide (N2O), and methane (CH4). [...] Read more.
Grasslands are subject to a wide range of land management practices that influence the exchange of the three main agricultural greenhouse gases (GHGs) that are related to agriculture: carbon dioxide (CO2), nitrous oxide (N2O), and methane (CH4). Improving nitrogen fertilization management practices through the use of nitrification inhibitors (NIs) can reduce GHGs emissions. We conducted a field experiment at the Colombian Agricultural Research Corporation with four fertilization treatments: urea (typical fertilizer used in this region), ammonium sulfate nitrate (ASN), ASN plus the NI 3,4-dimethylpyrazole phosphate (ASN+DMPP), and an unfertilized control. The highest grassland yields (1956 and 2057 kg DM ha−1, respectively) and apparent fertilizer nitrogen recoveries (34% and 33%, respectively) were generated by the conventional urea fertilizer and ASN+DMPP. Furthermore, the use of ASN+DMPP reduced the N2O emissions that were related to N fertilization to the level of the unfertilized treatment (ca. 1.5 g N2O-N ha−1), with a significant reduction of N-yield-scaled N2O emissions (ca. 20 g N2O-N kg N uptake−1). These results support the application of DMPP as an alternative strategy to increase grassland yield while simultaneously reducing the environmental impact of N fertilization. Full article
(This article belongs to the Special Issue Strategies for Greenhouse Gas Emissions Mitigation)
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<p>Daily greenhouse gas (GHGs) emissions (<b>A</b>–<b>C</b>), soil NH<sub>4</sub><sup>+</sup>-N (<b>D</b>) and NO<sub>3</sub><sup>−</sup>-N (<b>E</b>) contents, soil water-filled pore space (WFPS) (◊), soil temperature (♦) and rainfall (black bars) (<b>F</b>). Unfertilized (□), urea (△), ammonium sulfate nitrate (ASN) (○) and ASN plus the NI 3,4-dimethylpyrazole phosphate (ASN+DMPP) (●). Arrows show in order from left to right: sowing, fertilizer application and harvest. Vertical lines indicate least significant difference (LSD) (<span class="html-italic">p</span> &lt; 0.05; <span class="html-italic">n</span> = 4) for each sampling day.</p>
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<p>Daily CO<sub>2</sub> emissions for the different treatments related to WFPS (<b>A</b>) (<span class="html-italic">p</span> = 0.002) and soil temperature (<b>B</b>) (<span class="html-italic">p</span> = 0.000). Unfertilized (□), urea (△), ASN (○), and ASN+DMPP (●).</p>
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17 pages, 2174 KiB  
Article
Livestock Performance for Sheep and Cattle Grazing Lowland Permanent Pasture: Benchmarking Potential of Forage-Based Systems
by Robert J. Orr, Bruce A. Griffith, M. Jordana Rivero and Michael R. F. Lee
Agronomy 2019, 9(2), 101; https://doi.org/10.3390/agronomy9020101 - 21 Feb 2019
Cited by 21 | Viewed by 8921
Abstract
Here we describe the livestock performance and baseline productivity over a two-year period, following the establishment of the infrastructure on the North Wyke Farm Platform across its three farmlets (small farms). Lowland permanent pastures were continuously stocked with yearling beef cattle and ewes [...] Read more.
Here we describe the livestock performance and baseline productivity over a two-year period, following the establishment of the infrastructure on the North Wyke Farm Platform across its three farmlets (small farms). Lowland permanent pastures were continuously stocked with yearling beef cattle and ewes and their twin lambs for two years in three farmlets. The cattle came into the farmlets as suckler-reared weaned calves at 195 ± 32.6 days old weighing 309 ± 45.0 kg, were housed indoors for 170 days then turned out to graze weighing 391 ± 54.2 kg for 177 days. Therefore, it is suggested for predominantly grass-based systems with minimal supplementary feeding that target live weight gains should be 0.5 kg/day in the first winter, 0.9 kg/day for summer grazing and 0.8 kg/day for cattle housed and finished on silage in a second winter. The sheep performance suggested that lambs weaned at 100 days and weighing 35 kg should finish at 200 days weighing 44 to 45 kg live weight with a killing out percentage of 44%. Good levels of livestock production are possible with grass and forage-based systems using little or no additional supplementary concentrate feeds. Full article
(This article belongs to the Section Grassland and Pasture Science)
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<p>The NWFP farmlets: A (<span class="html-fig-inline" id="agronomy-09-00101-i001"> <img alt="Agronomy 09 00101 i001" src="/agronomy/agronomy-09-00101/article_deploy/html/images/agronomy-09-00101-i001.png"/></span>), B (<span class="html-fig-inline" id="agronomy-09-00101-i002"> <img alt="Agronomy 09 00101 i002" src="/agronomy/agronomy-09-00101/article_deploy/html/images/agronomy-09-00101-i002.png"/></span>) and C (<span class="html-fig-inline" id="agronomy-09-00101-i003"> <img alt="Agronomy 09 00101 i003" src="/agronomy/agronomy-09-00101/article_deploy/html/images/agronomy-09-00101-i003.png"/></span>); French drains (<span class="html-fig-inline" id="agronomy-09-00101-i004"> <img alt="Agronomy 09 00101 i004" src="/agronomy/agronomy-09-00101/article_deploy/html/images/agronomy-09-00101-i004.png"/></span>) and water monitoring sites (●).</p>
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<p>Solar radiation (MJ m<sup>−2</sup> day<sup>−1</sup>) measured in 2011 (<span class="html-fig-inline" id="agronomy-09-00101-i002"> <img alt="Agronomy 09 00101 i002" src="/agronomy/agronomy-09-00101/article_deploy/html/images/agronomy-09-00101-i002.png"/></span>) and 2012 (<span class="html-fig-inline" id="agronomy-09-00101-i005"> <img alt="Agronomy 09 00101 i005" src="/agronomy/agronomy-09-00101/article_deploy/html/images/agronomy-09-00101-i005.png"/></span>).</p>
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<p>Silage yield plus grazing offtake (kg DM ha<sup>−1</sup>) by cattle, ewes and lambs for farmlets A, B and C in 2011 and 2012.</p>
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17 pages, 2395 KiB  
Article
Evaluation of Evapotranspiration from Eddy Covariance Using Large Weighing Lysimeters
by Jerry E. Moorhead, Gary W. Marek, Prasanna H. Gowda, Xiaomao Lin, Paul D. Colaizzi, Steven R. Evett and Seth Kutikoff
Agronomy 2019, 9(2), 99; https://doi.org/10.3390/agronomy9020099 - 20 Feb 2019
Cited by 34 | Viewed by 5921
Abstract
Evapotranspiration (ET) is an important component in the water budget and used extensively in water resources management such as water planning and irrigation scheduling. In semi-arid regions, irrigation is used to supplement limited and erratic growing season rainfall to meet crop water demand. [...] Read more.
Evapotranspiration (ET) is an important component in the water budget and used extensively in water resources management such as water planning and irrigation scheduling. In semi-arid regions, irrigation is used to supplement limited and erratic growing season rainfall to meet crop water demand. Although lysimetery is considered the most accurate method for crop water use measurements, high-precision weighing lysimeters are expensive to build and operate. Alternatively, other measurement systems such as eddy covariance (EC) are being used to estimate crop water use. However, due to numerous explicit and implicit assumptions in the EC method, an energy balance closure problem is widely acknowledged. In this study, three EC systems were installed in a field containing a large weighing lysimeter at heights of 2.5, 4.5, and 8.5 m. Sensible heat flux (H) and ET from each EC system were evaluated against the lysimeter. Energy balance closure ranged from 64% to 67% for the three sensor heights. Results showed that all three EC systems underestimated H and consequently overestimated ET; however, the underestimation of H was greater in magnitude than the overestimation of ET. Analysis showed accuracy of ET was greater than energy balance closure with error rates of 20%–30% for half-hourly values. Further analysis of error rates throughout the growing season showed that energy balance closure and ET accuracy were greatest early in the season and larger error was found after plants reached their maximum height. Therefore, large errors associated with increased biomass may indicate unaccounted-for energy stored in the plant canopy as one source of error. Summing the half-hourly data to a daily time-step drastically reduced error in ET to 10%–15%, indicating that EC has potential for use in agricultural water management. Full article
(This article belongs to the Special Issue Crop Evapotranspiration)
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<p>Ogallala Aquifer and Texas High Plains Regions.</p>
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<p>Study location and layout of the lysimeters (yellow arrows) in the northeast (NE), southeast (SE), northwest (NW), and southwest (SW) quadrants with the location of the eddy covariance system in the NE quadrant.</p>
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<p>Energy balance closure for the three eddy covariance systems positioned at heights of 2.5 (<b>a</b>), 4.5 (<b>b</b>), and 8.5 (<b>c</b>) m above the soil surface, as indicated by the regression slope between the available energy (AE = <span class="html-italic">R<sub>n</sub></span> − G) and the turbulent fluxes (H + LE).</p>
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<p>Sensible heat flux (H) from the three eddy covariance (EC H) systems regressed against H calculated from the lysimeter for systems installed at 2.5 (<b>a</b>), 4.5 (<b>b</b>), and 8.5 m (<b>c</b>).</p>
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<p>Evapotranspiration (ET) averaged for each 30-min interval across the entire growing season for the lysimeter and the three eddy covariancesystems.</p>
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<p>Regression graphs for evapotranspiration from the lysimeter and eddy covariance (EC ET) for systems installed at 2.5 (<b>a</b>), 4.5 (<b>b</b>), and 8.5 m (<b>c</b>).</p>
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17 pages, 1983 KiB  
Article
Effect of Irrigation Regimes and Soil Texture on the Potassium Utilization Efficiency of Rice
by Yousef Alhaj Hamoud, Zhenchang Wang, Xiangping Guo, Hiba Shaghaleh, Mohamed Sheteiwy, Sheng Chen, Rangjian Qiu and Mohammed M. A. Elbashier
Agronomy 2019, 9(2), 100; https://doi.org/10.3390/agronomy9020100 - 20 Feb 2019
Cited by 43 | Viewed by 7395
Abstract
Understanding the effects of irrigation regime and soil texture on potassium-use efficiency (KUE) of rice (Oryza sativa. L) is essential for improving rice productivity. In this regard, experiments were conducted from July to October in 2016 and 2017 by using a [...] Read more.
Understanding the effects of irrigation regime and soil texture on potassium-use efficiency (KUE) of rice (Oryza sativa. L) is essential for improving rice productivity. In this regard, experiments were conducted from July to October in 2016 and 2017 by using a randomized complete block design in a factorial arrangement with four replications. The rice plants were grown in three soils, with clay contents of 40%, 50%, and 60%, which were marked as S (40%), S (50%), and S (60%), respectively. For each soil type, irrigation regimes, namely, R (F, S100%), R (F, S90%), and R (F, S70%), were established by setting the lower limit of irrigation to 100%, 90%, and 70% of saturated soil water content, respectively, and the upper limit of irrigation with 30 mm of flooding water above the soil surface for all irrigation regimes. Results showed that the responses of the roots and shoots and the potassium accumulation (KA) and KUE of rice were significantly affected by the water regime and soil texture. In the same irrigation regime, increasing the soil clay content improved the K utilization of rice. Under the same soil type, R (F, S100%) was the optimal water management practice for growing rice. The R (F, S100%) S (60%) treatment presented the highest KUE, which was 56.4% in 2016 and 68.1% in 2017. The R (F, S70%) S (40%) treatment showed the lowest KUE, which was 13.8% in 2016 and 14.9% in 2017. These results enrich knowledge regarding the relationship among soil, water, and rice, and provide valuable insights on the effect of irrigation regime and soil texture on the KA and KUE of rice. Full article
(This article belongs to the Section Water Use and Irrigation)
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<p>Experimental pot setup.</p>
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<p>The crack volume due to soil water content and soil clay content in 2017. Values are means ± SD. S <sub>(40%)</sub>, S <sub>(50%)</sub>, S <sub>(60%)</sub>, representing soil clay contents of 40%, 50%, and 60%, respectively. The means are not significantly different when followed by the same letter.</p>
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<p>Volumes of water at each irrigation according to the irrigation regime and soil texture in 2017. Error bars represent the mean standard error. During the same growth stage, means are not significantly different between different treatments (<span class="html-italic">P</span> ≤ 0.05), when followed by the same lowercase letter. During the same growth stage, the means are not significantly different between different irrigation regimes (<span class="html-italic">P</span> ≤ 0.05), according to the two-way ANOVA analysis, when followed by the same uppercase letter.</p>
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<p>Water percolation as affected by the irrigation regime and soil texture in 2017. Error bars represent the mean standard error. During the same growth stage, means are not significantly different between different treatments (<span class="html-italic">P</span> ≤ 0.05), when followed by the same lowercase letter. During the same growth stage, the means are not significantly different between different irrigation regimes (<span class="html-italic">P</span> ≤ 0.05), according to the two-way ANOVA analysis, when followed by the same uppercase letter.</p>
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<p>Plant biomass parameters for different irrigation regimes and soil types. The full-length column represents the total biomass.</p>
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<p>Potassium use efficiency, as affected by the irrigation regime and soil texture. During the same year, the means are not significantly different between different treatments (<span class="html-italic">P ≤ 0.05</span>) when followed by the same lowercase letter. During the same year, the means are not significantly different between different irrigation regimes (<span class="html-italic">P ≤ 0.05</span>) when followed by the same uppercase letter, according to the two-way ANOVA analysis.</p>
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21 pages, 730 KiB  
Article
Szarvasi-1 and Its Potential to Become a Substitute for Maize Which Is Grown for the Purposes of Biogas Plants in the Czech Republic
by Jaroslav Bernas, Jan Moudrý, Jr., Marek Kopecký, Petr Konvalina and Zdeněk Štěrba
Agronomy 2019, 9(2), 98; https://doi.org/10.3390/agronomy9020098 - 19 Feb 2019
Cited by 10 | Viewed by 3481
Abstract
The domestic biogas market has been developing rapidly, and legislation (The Act) supporting the use of renewable energy sources has come into force. In light of this act and investment support from national programs co-financed by the European Union (EU), the total number [...] Read more.
The domestic biogas market has been developing rapidly, and legislation (The Act) supporting the use of renewable energy sources has come into force. In light of this act and investment support from national programs co-financed by the European Union (EU), the total number of biogas plants has recently increased from a few to 600. The total capacity of electricity generation of those 600 installed plants exceeds 360 Megawatts (MW) (as of mid-2018). Such dynamic growth is expected to continue, and the targets of the National Renewable Energy Action Plan are projected to be met. The use of waste material, which was urgently needed, was the original aim of biogas plants. However, in certain cases, the original purpose has transformed, and phytomass is very often derived from purpose-grown energy crops. Maize is the most common and widely grown energy crop in the Czech Republic. Nevertheless, maize production raises several environmental issues. One way to potentially reduce maize’s harmful effects is to replace it with other suitable crops. Perennial energy crops, for example, are possible alternatives to maize. A newly introduced species for the conditions of the Czech Republic, Elymus elongatus subsp. ponticus cv. Szarvasi-1, and some other well-known species—Phalaris arundinacea L. and Miscanthus × giganteus—are suitable for Czech Republic climate conditions. This paper presents the findings of the research and evaluation of environmental, energy-related, and economic aspects of growing these crops for use in biogas plants. These findings are based on 5-year small-plot field trials. The energy-related aspects of producing Elymus elongatus subsp. ponticus cv. Szarvasi-1, Phalaris arundinacea L., and Miscanthus x giganteus are reported on the basis of experiments that included measuring the real methane yield from a production unit. The economic analysis is based on a model of every single growing and technological operation and costs. The environmental burden of the individual growing methods was assessed with a simplified life cycle assessment (LCA) using the impact category of Climate Change and the SimaPro 8.5.2.0 software tool, including an integrated method called ReCiPe. The research findings show that Szarvasi-1 produces 5.7–6.7 Euros (EUR) per Gigajoule (GJ) of energy, depending on the growing technology used. Szarvasi-1 generates an average energy profit of 101.4 GJ ha−1, which is half of that produced by maize (214.1 GJ ha−1). The environmental burden per energy unit of maize amounts to 16 kg of carbon dioxide eq GJ−1 compared with the environmental burden per energy unit of Szarvasi-1, which amounts to 7.2–15.6 kg of CO2 eq GJ−1, depending on the yield rate. On the basis of the above-mentioned yield rate of Szarvasi-1, it cannot be definitively recommended for the purpose of biogas plants in the Czech Republic. Full article
(This article belongs to the Special Issue Forage and Bioenergy Crops)
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<p>Contributions of every process to the total environmental burden (%).Identical contribution of RCG and Sz-1 and every process to the total environmental burden (%) is caused by identical farming technology used.</p>
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<p>Emissions per area unit. PY = productive year, YoE = year of establishment.</p>
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12 pages, 575 KiB  
Article
Effect of Vermicompost Application on Bioactive Properties and Antioxidant Potential of MD2 Pineapple Fruits
by Mawiyah Mahmud, Sujatha Ramasamy, Rashidi Othman, Rosazlin Abdullah and Jamilah Syafawati Yaacob
Agronomy 2019, 9(2), 97; https://doi.org/10.3390/agronomy9020097 - 19 Feb 2019
Cited by 9 | Viewed by 4520
Abstract
Vermicompost is an organic waste produced from earthworms that can enhance the soil condition and is rich with essential plant nutrients, thus increasing produce quality and shelf life. In this study, a one-year field trial was conducted to elucidate the effects of vermicompost [...] Read more.
Vermicompost is an organic waste produced from earthworms that can enhance the soil condition and is rich with essential plant nutrients, thus increasing produce quality and shelf life. In this study, a one-year field trial was conducted to elucidate the effects of vermicompost supplementation on the composition of bioactive compounds and antioxidant activities of pineapple (Ananas comosus var. MD2) fruits, compared to control and application of chemical fertilizer. Based on the results, pineapple fruits produced from plants supplemented with chemical fertilizer showed the strongest radical scavenging properties against 2,2-Diphenyl-1-picrylhydrazyl (DPPH) and 2,2′-azinobis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS), followed by vermicompost and control plants. Application of chemical fertilizer and vermicompost also produced fruits with a very high content of chlorophylls and β-carotene compared to control plants. However, the amounts of bioactive compounds present in fruits produced with chemical fertilizer are higher than in fruits produced with vermicompost. Total phenolics content and Ferric Reducing Antioxidant Power (FRAP) reducing power were lowest in fruit extracts produced from pineapple plants supplemented with vermicompost. These results suggested that vermicompost cannot completely replace chemical fertilizer for the production of fruits with a high content of phytoconstituents but could be used as an additional supplement to reduce environmental pollution and ensure agricultural sustainability. Full article
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<p>HPLC chromatograms of carotenoids in pineapple pulp extracts retrieved from plants treated with different types of fertilizers in the field. (<b>A</b>) control (<b>B</b>) chemical fertilizer (<b>C</b>) vermicompost. The peak height of less than 10 mAU was not detected.</p>
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18 pages, 5654 KiB  
Article
An Automated Plot Heater for Field Frost Research in Cereals
by Bonny M. Stutsel, John Nikolaus Callow, Ken Flower, Thomas Ben Biddulph, Ben Cohen and Brenton Leske
Agronomy 2019, 9(2), 96; https://doi.org/10.3390/agronomy9020096 - 19 Feb 2019
Cited by 6 | Viewed by 4350
Abstract
Frost research to improve genetics or management solutions requires a robust experimental design that minimizes the effects of all other variables that can cause plant damage except for the treatment (frost). Controlled environment facilities cannot faithfully replicate field radiative frost processes, but field [...] Read more.
Frost research to improve genetics or management solutions requires a robust experimental design that minimizes the effects of all other variables that can cause plant damage except for the treatment (frost). Controlled environment facilities cannot faithfully replicate field radiative frost processes, but field studies are limited by the reliability of field methods to exclude frost. An effective field frost exclusion method needs to prevent frost damage while not impacting growing microclimate or yield, and be automatic, modular, mobile, and affordable. In this study, we designed an effective prototype treatment with these features for field frost research that uses diesel heating. The effectiveness of the plot heater to provide an unfrosted control is evaluated by monitoring canopy temperature (CT) and air temperature during frost events, showing that these remain above zero in the heated plots when ambient temperature drops below zero. We find that the plot heater method can prevent potential frost damage at the plot-scale, while not appearing to have an impact on either plant development or yield components. This offers a potential new tool for frost field crop researchers to incorporate a plot-scale control into their experimental design. Full article
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<p>(<b>a</b>) Image of the air diffuser manifold (PVC pipes). (<b>b</b>) Image of heater set up inside of the waterproof casing. (<b>c</b>) Diesel plot heater diagram (plan view); A. Air diffuser manifold (PVC pipes) (note that while pipes are drawn to the same length pipes two and four are slightly longer due to the piece used to attach theses pipes to the manifold base), B. Waterproof casing with lid, C. 12 V 2 KW diesel caravan air heater, D. 5 L diesel fuel canister, E. PT-100 temperature probe (at flag leaf height) and 12 V DC digital thermostat temperature controller, F. 120 Ah 12 V AGM battery, G. Victron BlueSolar MPPT 75/15 solar charge controller, H. 12 V 120W solar panel. Note: green lines are where the rows of wheat would be within the plot.</p>
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<p>Diesel plot heater set up in a plot at the Dale frost nursery.</p>
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<p>Plot layout at the Dale trial site with the first and second location of heater one (H-1), heater two (H-2) and heater three (H-3) is shown.</p>
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<p>Dale weather station data for the 3 September and 6 October 2017 and 15, 16 September 2018, with light blue dotted line at 2 °C and 0 °C to show how frost is categorized at screen height. Note: In 2017 the weather station thermocouple was at 600 mm AGL and at 500 mm AGL in 2018.</p>
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<p>Canopy temperature recorded by thermal infrared Arducrop<sup>®</sup> (10° FOV) for the heated plot (solid line) and equivalent unheated plot (dashed line) on 3 September 2017 (<b>A</b>,<b>C</b>, and <b>E</b>) and 6 October 2017 (<b>B</b>,<b>D</b>, and <b>F</b>) for heaters number one (<b>A</b>,<b>B</b>), two (<b>C</b>,<b>D</b>) and three (<b>E</b>,<b>F</b>).</p>
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<p>Air temperature record by T-Type thermocouples at 400, 600 and 800 mm AGL for the heated plot (solid line) and adjacent unheated plot (dashed line) on 15 September (<b>A</b>,<b>C</b>,<b>E</b>,<b>G</b>,<b>I</b>, and <b>K</b>) and 16 September 2018 (<b>B</b>,<b>D</b>,<b>F</b>,<b>H</b>,<b>J</b>, and <b>L</b>) for heaters number two (<b>A</b>–<b>F</b>) and three (<b>G</b>–<b>L</b>). Note heater 1 not deployed in 2018.</p>
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<p>Thermal image captured by a drone-mounted FLIR Tau 2 640 25 mm camera above each heater between 6:17 to 6:21 am on 27 September 2017. (<b>A</b>) Heater one in ToS 7 (Wyalkatchem), (<b>B</b>) Heater two in ToS 5 (Wyalkatchem) and, (<b>C</b>) Heater three in ToS7 (Elmore). Note: The square areas with temperature ≥10 °C behind the heaters correspond components of the plot heaters (solar panel and waterproof box), and the area above heater three is a thermal ground control target panel.</p>
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<p>Mean FS in ToS 3,4,5 and 7 for both Wyalkatchem and Elmore in 2017, with standard error bars of the mean (<span class="html-italic">n</span> = 90, 30 heads from each replicate, 3 replicates per ToS).</p>
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<p>Mean HI, total biomass(g/m<sup>2</sup>), grain yield (g/m<sup>2</sup>), number of grains per m<sup>2</sup>, thousand grain weight and number of productive tillers per m<sup>2</sup> for the 6 heated plots and non-heated mirror plots for 2017, with error bars representing standard error of the mean. <span class="html-italic">p</span>-value results from a paired <span class="html-italic">t</span>-test are report for the difference between the means. (<span class="html-italic">n</span> = 6).</p>
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16 pages, 1317 KiB  
Review
Genomic Selection in Cereal Breeding
by Charlotte D. Robertsen, Rasmus L. Hjortshøj and Luc L. Janss
Agronomy 2019, 9(2), 95; https://doi.org/10.3390/agronomy9020095 - 19 Feb 2019
Cited by 78 | Viewed by 13723
Abstract
Genomic Selection (GS) is a method in plant breeding to predict the genetic value of untested lines based on genome-wide marker data. The method has been widely explored with simulated data and also in real plant breeding programs. However, the optimal strategy and [...] Read more.
Genomic Selection (GS) is a method in plant breeding to predict the genetic value of untested lines based on genome-wide marker data. The method has been widely explored with simulated data and also in real plant breeding programs. However, the optimal strategy and stage for implementation of GS in a plant-breeding program is still uncertain. The accuracy of GS has proven to be affected by the data used in the GS model, including size of the training population, relationships between individuals, marker density, and use of pedigree information. GS is commonly used to predict the additive genetic value of a line, whereas non-additive genetics are often disregarded. In this review, we provide a background knowledge on genomic prediction models used for GS and a view on important considerations concerning data used in these models. We compare within- and across-breeding cycle strategies for implementation of GS in cereal breeding and possibilities for using GS to select untested lines as parents. We further discuss the difference of estimating additive and non-additive genetic values and its usefulness to either select new parents, or new candidate varieties. Full article
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<p>Overview of genomic selection with cross validation using a training population to estimate marker effects in order to get a genomic estimated breeding value (GEBV) of lines in the test-population.</p>
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<p>Standard breeding scheme showing one cross using double haploid lines, e.g., barley. Triangles indicate steps where material is selected and reduced using genomic selection. P1 = Parent one, P2 = Parent 2, F1 = offspring/hybrid, DH = Double Haploids, PYT = Preliminary Yield Trial, AYT = Advanced Yield Trial, YET = Elite Yield Trial. Photos of field trials on breeding station.</p>
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<p>Use of genomic selection across generations based on a standard cereal breeding scheme. Red curved arrows show how information for GS could be used across generations. DH=Double Haploids, PYT = Preliminary Yield Trial, AYT = Advanced Yield Trial, YET = Elite Yield Trial, Yr = Year.</p>
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21 pages, 2763 KiB  
Article
Description and Preliminary Simulations with the Italian Vineyard Integrated Numerical Model for Estimating Physiological Values (IVINE)
by Valentina Andreoli, Claudio Cassardo, Tiziana La Iacona and Federico Spanna
Agronomy 2019, 9(2), 94; https://doi.org/10.3390/agronomy9020094 - 18 Feb 2019
Cited by 20 | Viewed by 4558
Abstract
The numerical crop growth model Italian Vineyard Integrated Numerical model for Estimating physiological values (IVINE) was developed in order to evaluate environmental forcing effects on vine growth. The IVINE model simulates vine growth processes with parameterizations, allowing the understanding of plant conditions at [...] Read more.
The numerical crop growth model Italian Vineyard Integrated Numerical model for Estimating physiological values (IVINE) was developed in order to evaluate environmental forcing effects on vine growth. The IVINE model simulates vine growth processes with parameterizations, allowing the understanding of plant conditions at a vineyard scale. It requires a set of meteorology data and soil water status as boundary conditions. The primary model outputs are main phenological stages, leaf development, yield, and sugar concentration. The model requires setting some variety information depending on the cultivar: At present, IVINE is optimized for Vitis vinifera L. Nebbiolo, a variety grown mostly in the Piedmont region (northwestern Italy). In order to evaluate the model accuracy, IVINE was validated using experimental observations gathered in Piedmontese vineyards, showing performances similar or slightly better than those of other widely used crop models. The results of a sensitivity analysis performed to highlight the effects of the variations of air temperature and soil water potential input variables on IVINE outputs showed that most phenological stages anticipated with increasing temperatures, while berry sugar content saturated at about 25.5 °Bx. Long-term (60 years, in the period 1950–2009) simulations performed over a Piedmontese subregion showed statistically significant variations of most IVINE output variables, with larger time trend slopes referring to the most recent 30-year period (1980–2009), thus confirming that ongoing climate change started influencing Piedmontese vineyards in 1980. Full article
(This article belongs to the Special Issue Viticulture and Winemaking under Climate Change)
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<p>Location of the 15 grid points over the Piedmontese territory.</p>
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<p>Comparison between simulated and measured leaf area index (<span class="html-italic">LAI</span>) in the Castiglione Falletto site.</p>
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<p>Comparison between simulated and measured berry weight (g) in the Castagnito site during 2005 (<b>a</b>), and in the Castagnito site during 2007 (<b>b</b>).</p>
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<p>Comparison between simulated and measured berry sugar content (°Bx) in the Castagnito site during 2004 (<b>a</b>) and 2005 (<b>b</b>).</p>
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<p>Sensitivity to changes of air temperature on the date of the flowering phase (expressed in Julian days) (<b>a</b>) and on the berry sugar content (in °Bx) evaluated at the 287th Julian day (corresponding to October 14th) (<b>b</b>). <span class="html-italic">ΔT<sub>air</sub></span> is the difference between the input temperature and the actual temperature record.</p>
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<p>Sensitivity to changes in soil water potential on the maximum <span class="html-italic">LAI</span> (expressed in m<sup>2</sup> m<sup>−2</sup>) (<b>a</b>) and on the yield/vine (expressed in kg) (<b>b</b>). <span class="html-italic">ΔΨ</span> is the difference between the input soil water potential and the actual soil water potential record. Note that 1 m of water potential (hydraulic head) corresponds to about 0.01 MPa of suction.</p>
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<p>Flowering date (expressed in Julian days) (<b>a</b>), berry sugar content (<b>b</b>) at the date of 287th Julian day (expressed in °Bx) (<b>c</b>), <span class="html-italic">LAI</span> maximum value (expressed in m<sup>2</sup> m<sup>−2</sup>), and yield per vine (expressed in kg) (<b>d</b>), simulated by IVINE in three grid points characterized by different elevation. Cv.: Nebbiolo.</p>
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<p>Flowering date (expressed in Julian days) (<b>a</b>), berry sugar content at the date of 287th Julian day (expressed in °Bx) (<b>b</b>), <span class="html-italic">LAI</span> maximum value (expressed in m<sup>2</sup> m<sup>−2</sup>) (<b>c</b>), and yield per vine (expressed in kg) (<b>d</b>), simulated by IVINE in two grid points characterized by different soil texture. Cv.: Nebbiolo.</p>
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23 pages, 414 KiB  
Article
Morphological and Biochemical Responses of Glycine max (L.) Merr. to the Use of Seaweed Extract
by Sławomir Kocira, Agnieszka Szparaga, Maciej Kuboń, Ewa Czerwińska and Tomasz Piskier
Agronomy 2019, 9(2), 93; https://doi.org/10.3390/agronomy9020093 - 18 Feb 2019
Cited by 42 | Viewed by 7608
Abstract
Currently, modern agriculture aims to improve the quantity and quality of crop yield, while minimizing the negative impact of treatments on the natural environment. One of the methods to increase plant yield and quality, especially after the occurrence of both abiotic or biotic [...] Read more.
Currently, modern agriculture aims to improve the quantity and quality of crop yield, while minimizing the negative impact of treatments on the natural environment. One of the methods to increase plant yield and quality, especially after the occurrence of both abiotic or biotic stress factors, is the application of biostimulants. The aim of the study was to determine the effect of Ecklonia maxima extract on plant growth, and the yield, nutritional, and nutraceutical properties of soybean seeds. A field experiment was conducted in three growing seasons (2014–2016). Soybean seeds of Atlanta cultivar were sown in the third 10-day period of April. Ecklonia maxima extract was applied in the form of single or double, spraying in the concentrations of 0.7% and 1.0%. Determinations were conducted for: biometric traits, seed yield, seed number, thousand seeds weight, contents of lipids, and proteins in seeds. Further analyses included the contents of total polyphenols, flavonoids, anthocyanins, and reducing power. The number of seaweed extract applications and its concentration modified biometric traits, yield, and quality of crop, while also also altering the nutraceutical and antioxidative potential of soybean. The application of this preparation improved the growth and yield of soybean without any negative effect on the nutritive value of seeds. Full article
14 pages, 2373 KiB  
Article
Selection of Salicylic Acid Tolerant Epilines in Brassica napus
by Sonja Klemme, Yorick De Smet, Bruno P. A. Cammue and Marc De Block
Agronomy 2019, 9(2), 92; https://doi.org/10.3390/agronomy9020092 - 18 Feb 2019
Cited by 4 | Viewed by 4175
Abstract
Two of the major pathways involved in induced defense of plants against pathogens include the salicylic acid (SA)- and jasmonic acid (JA)-mediated pathways that act mainly against biotrophs and necrotrophs, respectively. However, some necrotrophic pathogens, such as Botrytis cinerea, actively induce the [...] Read more.
Two of the major pathways involved in induced defense of plants against pathogens include the salicylic acid (SA)- and jasmonic acid (JA)-mediated pathways that act mainly against biotrophs and necrotrophs, respectively. However, some necrotrophic pathogens, such as Botrytis cinerea, actively induce the SA pathway, resulting in cell death that allows the pathogen to proliferate in the plant. Starting from an isogenic canola (Brassica napus) line, epilines were selected with a reduced sensitivity to SA. The genes belonging to the SA pathway had an altered transcription profile in the SA-tolerant lines, when treated with SA. Besides the already known genes of the SA pathway, new SA target genes were identified, creating possibilities to better understand the plant defense mechanism against pathogens. The SA-tolerant line with the lowest SA-induced gene expression is tolerant to Botrytis cinerea. When treated with SA, this line has also a reduced histone modification (histone H3 lysine 4 trimethylation) at the genes at the start of the SA pathway. Full article
(This article belongs to the Special Issue The Regulatory Functions of Epigenetic Mechanisms in Plants)
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<p>Salicylic acid and jasmonic acid pathways react to different types of pathogens. (<b>A</b>) The plant responds to biotrophic pathogens via the SA pathway, inducing SA-specific pathogen response genes, resulting in hypersensitive responses that inhibit the spread of the pathogens. The response to necrotrophic pathogens occurs via the JA pathway, inducing JA-specific pathogen response genes that block the hypersensitive response. (<b>B</b>) Certain necrotrophic fungi actively induce the SA pathway to elicit the hypersensitive response and feed on the dead cells, while they also block the JA pathway.</p>
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<p>Salicylic acid (SA) pathway. SA induces the expression and transfer to the nucleus of NPR1, where it interacts with the kinases NIMIN1&amp;2 in a complex to trigger WRKY70. This transcription factor is responsible for the transcription of pathway-specific pathogen resistance genes, such as <span class="html-italic">PR1</span> and <span class="html-italic">PR2</span>.</p>
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<p>Weight loss (<b>A</b>) and increase of ascorbate concentration per fresh weight (<b>B</b>) in cotyledons of 12-day-old seedlings of the control line treated for 16 h with 50 mg/L SA. 45 cotyledons derived from 45 seedlings were used per condition. Statistical significance versus control using unpaired <span class="html-italic">t</span>-test (GraphPad Prism version 7.00 for OS X, GraphPad Software, La Jolla California USA, <a href="http://www.graphpad.com" target="_blank">www.graphpad.com</a>): *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>Tolerance to <span class="html-italic">Botrytis cinerea</span>. (<b>A</b>–<b>C</b>) Qualitative spray assay. Four-week old plants were sprayed with 8 × 10<sup>8</sup> spores/mL of <span class="html-italic">Botrytis cinerea</span>. The pictures were taken four days after spraying. The leaf damage (necrosis and chlorosis) was scored by quantifying the non-green area versus the total leaf area using image analysis. In the selected lines (<b>A</b>,<b>B</b>), there is less leaf damage four days after infection than in the control (<b>C</b>), indicating less spread of the fungus in the leaves. The leaves derived from 18 treated plants per line are shown. (<b>D</b>) Quantitative spot assay. 2 × 5 µL of 1 × 10<sup>7</sup> spores of <span class="html-italic">Botrytis cinerea</span> were spotted on both cotyledons of 12-day-old seedlings. Lesion diameter was measured 7 days after inoculation. Both selected lines were compared to the control. The horizontal error bars show the standard deviation. Statistical significance <span class="html-italic">versus</span> control with ANOVA and Tukey’s post-hoc test: * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, and *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>Expression of known SA target genes (<a href="#agronomy-09-00092-f002" class="html-fig">Figure 2</a>) in the control and selected Lines 1 and 2 after treatment with 50 mg/L SA. The samples were taken at fixed time points after treatment. For each time point three biological replicates and three technical repeats per biological repeat were used. Error bars indicate standard deviation (<span class="html-italic">n</span> = 3).</p>
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<p>Expression of newly identified SA target genes in the control, and selected Lines 1 and 2 after treatment with 50 mg/L SA. The samples were taken at fixed time points after treatment. For each time point three biological replicates and three technical repeats per biological repeat were used. Error bars indicate standard deviation (<span class="html-italic">n</span> = 3).</p>
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<p>H3K4me3 deposition, marker for activation of transcription, on the SA target genes WRKY70 and PR1 in the control, and selected Lines 1 and 2. The SA pathway was induced by treating the seedlings with 50 mg/L SA. Samples were taken 6 h post induction at which point all known targets were expressed. Two independent replicates are shown, represented by two colors.</p>
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28 pages, 11142 KiB  
Article
Unsupervised Greenhouse Tomato Plant Segmentation Based on Self-Adaptive Iterative Latent Dirichlet Allocation from Surveillance Camera
by Qifan Cao and Lihong Xu
Agronomy 2019, 9(2), 91; https://doi.org/10.3390/agronomy9020091 - 16 Feb 2019
Cited by 6 | Viewed by 3956
Abstract
It has long been a great concern in deep learning that we lack massive data for high-precision training sets, especially in the agriculture field. Plants in images captured in greenhouses, from a distance or up close, not only have various morphological structures but [...] Read more.
It has long been a great concern in deep learning that we lack massive data for high-precision training sets, especially in the agriculture field. Plants in images captured in greenhouses, from a distance or up close, not only have various morphological structures but also can have a busy background, leading to huge challenges in labeling and segmentation. This article proposes an unsupervised statistical algorithm SAI-LDA (self-adaptive iterative latent Dirichlet allocation) to segment greenhouse tomato images from a field surveillance camera automatically, borrowing the language model LDA. Hierarchical wavelet features with an overlapping grid word document design and a modified density-based method quick-shift are adopted, respectively, according to different kinds of images, which are classified by specific proportions between fruits, leaves, and the background. We also utilize the feature correlation between several layers of the image to make further optimization through three rounds of iteration of LDA, with updated documents to achieve finer segmentation. Experiment results show that our method can automatically label the organs of the greenhouse plant under complex circumstances, fast and precisely, overcoming the difficulty of inferior real-time image quality caused by a surveillance camera, and thus obtain large amounts of valuable training sets. Full article
(This article belongs to the Section Horticultural and Floricultural Crops)
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<p>LDA Bayesian network structure.</p>
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<p>The generative procedure of LDA.</p>
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<p>LDA segmentation algorithm flow.</p>
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<p>Process of word document encoding based on fixed non-overlapping grid. (<b>a</b>) Original image; (<b>b</b>) Consider the whole image as one document; (<b>c</b>) Quantified visual words; (<b>d</b>) Word document model: <math display="inline"><semantics> <mrow> <mi>α</mi> </mrow> </semantics></math> is the prior parameter for the document topic distribution;<math display="inline"><semantics> <mrow> <mo> </mo> <mi>θ</mi> </mrow> </semantics></math> is the multinomial parameter for the topic distribution; z is the corresponding topic for the denoted word; (<b>e</b>) Segmentation result.</p>
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<p>Extraction of tomato organs: algebraic operations in RGB color space.</p>
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<p>Proportion of tomato organs.</p>
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<p>Distance determination (DD score).</p>
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<p>Sub-bands separated by a three-level dyadic discrete wavelet transform (DWT).</p>
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<p>Word document encoding based on an improved document partition. (<b>a</b>) The traditional LDA document construction regards the whole image as a single document; (<b>b</b>) Overlapping rectangular patches are adopted with interval <span class="html-italic">a</span> to construct documents, where <span class="html-italic">a</span> = 25 pixel.</p>
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<p>(<b>a</b>) Histogram of the original image; (<b>b</b>) histogram after the homomorphic filtering.</p>
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<p>Mode seeking algorithm. (<b>a</b>) Mean shift: modes converge by estimating probability density kernels; (<b>b</b>) medoid shift: local modal approximation based on sample point-neighborhood weighted estimation; (<b>c</b>) quick shift: directly connect the sampling point to the highest density one.</p>
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<p>(<b>a</b>) High-density regions may have variations and too many apparent modes appear, leading to over-segmentation; (<b>b</b>) using modal-sets (shaded) not only allows disturbances within limits of different shape of regions of high density, but also matches various density levels, regarding them as the same cluster.</p>
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<p>The structure of the image pyramid. L stands for the image layer, from the original (L = 0) to coarser ones by continuously down-sampling.</p>
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<p>The flowchart of self-adaptive iterative latent Dirichlet allocation algorithm (SAI-LDA). DD is the distance determination; DWPT is discreet wavelet packet transform.</p>
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<p>The greenhouse environment. (<b>a</b>) The installation site of our fixed camera; (<b>b</b>–<b>d</b>) different growth stages and shooting angle of tomato plants.</p>
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<p>The cruise trajectory (red line with arrows) of our shooting strategy: moving up and down with varied focal length from the first row to the last.</p>
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<p>The result of different algorithms with distant shots. Column (<b>a</b>) are original images. Column (<b>b</b>) are high quality ground-truth. Columns (<b>c</b>–<b>h</b>) are the results of several segmentation methods: quantum-behaved particle swarm optimization (QPSO), fuzzy c-means clustering (FCM), pulse coupled neural network-based segmentation (PCNN), co-segmentation, the traditional latent Dirichlet allocation (LDA), and self-adaptive latent Dirichlet allocation (SA-LDA), respectively.</p>
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<p>The result of different algorithms with close shots. Column (<b>a</b>) are original images. Column (<b>b</b>) are high quality ground-truth. Columns (<b>c</b>–<b>h</b>) are the results of several segmentation methods: quantum-behaved particle swarm optimization (QPSO), fuzzy c-means clustering (FCM), pulse coupled neural network-based segmentation (PCNN), co-segmentation, the traditional latent Dirichlet allocation (LDA), and self-adaptive latent Dirichlet allocation (SA-LDA), respectively.</p>
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<p>The result of different algorithms with distant shots. Column (<b>a</b>) are original images. Column (<b>b</b>) are high quality ground-truth. Columns (<b>c</b>–<b>i</b>) are the results of several segmentation methods: quantum-behaved particle swarm optimization (QPSO), fuzzy c-means clustering (FCM), pulse coupled neural network based segmentation (PCNN), co-segmentation, the traditional latent Dirichlet allocation (LDA), self-adaptive latent Dirichlet allocation (SA-LDA), and self-adaptive iterative latent Dirichlet allocation (SAI-LDA), respectively.</p>
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<p>The result of different algorithms with close shots. Column (<b>a</b>) are original images. Column (<b>b</b>) are high quality ground-truth. Columns (<b>c</b>–<b>i</b>) are the results of several segmentation methods: quantum-behaved particle swarm optimization (QPSO), fuzzy c-means clustering (FCM), pulse coupled neural network based segmentation (PCNN), co-segmentation, the traditional latent Dirichlet allocation (LDA), self-adaptive latent Dirichlet allocation (SA-LDA), and self-adaptive iterative latent Dirichlet allocation (SAI-LDA), respectively.</p>
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<p>The result of fruit accuracy with different algorithms.</p>
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<p>The result of leaf accuracy with different algorithms.</p>
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<p>Comparison of <span class="html-italic">F<sub>1</sub></span>-measure value.</p>
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18 pages, 2199 KiB  
Article
Estimating Body Condition Score in Dairy Cows From Depth Images Using Convolutional Neural Networks, Transfer Learning and Model Ensembling Techniques
by Juan Rodríguez Alvarez, Mauricio Arroqui, Pablo Mangudo, Juan Toloza, Daniel Jatip, Juan M. Rodriguez, Alfredo Teyseyre, Carlos Sanz, Alejandro Zunino, Claudio Machado and Cristian Mateos
Agronomy 2019, 9(2), 90; https://doi.org/10.3390/agronomy9020090 - 16 Feb 2019
Cited by 55 | Viewed by 7589
Abstract
BCS (Body Condition Score) is a method to estimate body fat reserves and accumulated energy balance of cows, placing estimations (or BCS values) in a scale of 1 to 5. Periodically rating BCS of dairy cows is very important since BCS values are [...] Read more.
BCS (Body Condition Score) is a method to estimate body fat reserves and accumulated energy balance of cows, placing estimations (or BCS values) in a scale of 1 to 5. Periodically rating BCS of dairy cows is very important since BCS values are associated with milk production, reproduction, and health of cows. However, in practice, obtaining BCS values is a time-consuming and subjective task performed visually by expert scorers. There have been several efforts to automate BCS of dairy cows by using image analysis and machine learning techniques. In a previous work, an automatic system to estimate BCS values was proposed, which is based on Convolutional Neural Networks (CNNs). In this paper we significantly extend the techniques exploited by that system via using transfer learning and ensemble modeling techniques to further improve BCS estimation accuracy. The improved system has achieved good estimations results in comparison with the base system. Overall accuracy of BCS estimations within 0.25 units of difference from true values has increased 4% (up to 82%), while overall accuracy within 0.50 units has increased 3% (up to 97%). Full article
(This article belongs to the Special Issue Deep Learning Techniques for Agronomy Applications)
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<p>Overview of developed BCS estimation system [<a href="#B15-agronomy-09-00090" class="html-bibr">15</a>].</p>
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<p>Percentage of BCS values distribution over training and test set.</p>
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<p>CNN architecture model implemented in previous work Rodríguez Alvarez et al. [<a href="#B15-agronomy-09-00090" class="html-bibr">15</a>] (based on SqueezeNet [<a href="#B23-agronomy-09-00090" class="html-bibr">23</a>]). Description of SqueezeNet “Fire” module, and structure of the CNN model from its input (preprocessed image) to the final predicted class or BCS value (class with highest probability according to the input image).</p>
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<p>Transfer Learning: Architecture of the models taken into account. Keras support to graph models [<a href="#B29-agronomy-09-00090" class="html-bibr">29</a>] was used.</p>
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<p>Transfer learning/Fine-Tuning: Convolutional Base of VGG16 model where layers selected to re-train are highlighted in yellow. Keras support to graph models [<a href="#B29-agronomy-09-00090" class="html-bibr">29</a>] was used.</p>
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<p>Confusion matrices of test samples classification. Red cells represent exact predictions, orange cells represent predictions with 0.25 units of error, and yellow cells represent predictions with 0.50 units of error.</p>
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20 pages, 2211 KiB  
Article
Soil Amendment with Raw Garlic Stalk: A Novel Strategy to Stimulate Growth and the Antioxidative Defense System in Monocropped Eggplant in the North of China
by Muhammad Imran Ghani, Ahmad Ali, Muhammad Jawaad Atif, Muhammad Ali, Bakht Amin, Muhammad Anees and Zhihui Cheng
Agronomy 2019, 9(2), 89; https://doi.org/10.3390/agronomy9020089 - 15 Feb 2019
Cited by 18 | Viewed by 4133
Abstract
Garlic (Allium Sativum L.) is a vegetable with known medicinal value. It is not only rich in nutrients, but also has the ability to combat different microbial infections. This is, however, the first study to investigate the effect of soil incorporation of [...] Read more.
Garlic (Allium Sativum L.) is a vegetable with known medicinal value. It is not only rich in nutrients, but also has the ability to combat different microbial infections. This is, however, the first study to investigate the effect of soil incorporation of the raw garlic stalk (RGS) on the growth and antioxidative defense system of eggplant. The experiments were conducted in pots using soil amendments of RGS in different ratios (RGS1 1:100; RGS2 3:100; RGS3 5:100 and control (CK) 0:100 of RGS: Soil w/w) and repeated in two consecutive years (2016 and 2017). A dose-dependent response of RGS amendment was observed in the growth and physiology of the eggplant. RGS1 and RGS2 significantly enhanced the plant height, root/shoot weight, stem diameter, leaf area, root length, root activity, pigment contents (chlorophyll a, chlorophyll b, and total chlorophyll), and photosynthetic parameters, but reduced intracellular CO2 (Ci) and enhanced fruit yield as compared with the respective controls. Consistently, RGS also enhanced activities of antioxidative enzymes of eggplant reported as a defense against stress indicators. RGS in its higher ratios (RGS3), however, caused a reduction in all of the growth and physiological parameters and increased stress indicators such as hydrogen peroxide (H2O2) and malondialdehyde (MDA). Overall, RGS2 was found to be the most efficient for regulation of plant defense systems, reducing H2O2 and MDA and enhancing superoxide dismutase (SOD), peroxidase (POD), and phenylalanine ammonia–lyase (PAL) activity. It can be concluded that the appropriate ratio of RGS could efficiently promote plant growth and regulate the reactive oxygen-based plant defense system. Full article
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<p>Effect of amendment with raw garlic stalk on various growth and development parameters of eggplant recorded at different growth stage of eggplant. Plant height (<b>A</b>) 1st flowering, (<b>B</b>) 1st fruiting, (<b>C</b>) 2nd flowering and (<b>D</b>) 2nd fruiting, stem diameter (<b>E</b>) 1st flowering, (<b>F</b>) 1st fruiting, (<b>G</b>) 2nd flowering and (<b>H</b>) 2nd fruiting, leaf area (<b>I</b>) 1st flowering, (<b>J</b>) 1st fruiting, (<b>K</b>) 2nd flowering and (<b>L</b>) 2nd fruiting. The error bars represent standard error of the means (<span class="html-italic">n</span> = 3). Different letters between treatments at different sampling stages show significant difference at <span class="html-italic">p</span> &lt; 0.05 level (ANOVA and LSD).</p>
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<p>Effect of amendment with raw garlic stalk on root length of eggplant. The error bars represent standard error of the means (<span class="html-italic">n</span> = 3). The error bars represent standard of the means. Different letters between treatments at different sampling stages show significant difference at <span class="html-italic">p</span> &lt; 0.05 level (ANOVA and LSD).</p>
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<p>Effect of amendment with raw garlic stalk on shoot fresh and dry weight. Shoot fresh and dry weight (<b>A</b>,<b>B</b>). Root fresh and dry weight (<b>C</b>,<b>D</b>). The error bars represent standard error of the means (<span class="html-italic">n</span> = 3). Different letters between treatments at different sampling stages show significant difference at <span class="html-italic">p</span> &lt; 0.05 level (ANOVA and LSD).</p>
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<p>Effect of amendment with raw garlic stalk on root activity of eggplant. The error bars represent standard error of the means (<span class="html-italic">n</span> = 3). Different letters between treatments at different sampling stages show significant difference at <span class="html-italic">p</span> &lt; 0.05 level (ANOVA and LSD).</p>
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<p>Effect of amendment with raw garlic stalk on different gas exchange parameters of eggplant. Net photosynthesis rate (Pn) (<b>A</b>) 1st flowering, (<b>B</b>) 1st fruiting, (<b>C</b>) 2nd flowering and (<b>D</b>) 2nd fruiting, stomatal conductance (Gs) during (<b>E</b>) 1st flowering, (<b>F</b>) 1st fruiting, (<b>G</b>) 2nd flowering and (<b>H</b>) 2nd fruiting, internal CO<sub>2</sub> (Ci) (<b>I</b>) 1st flowering, (<b>J</b>) 1st fruiting, (<b>K</b>) 2nd flowering and (<b>L</b>) 2nd fruiting, transpiration rate (<b>E</b>) (<b>M</b>) 1st flowering, (<b>N</b>) 1st fruiting, (<b>O</b>) 2nd flowering and (<b>P</b>) 2nd fruiting. The error bars represent standard error of the means (<span class="html-italic">n</span> = 3). Different letters between treatments at different sampling stages show significant difference at <span class="html-italic">p</span> &lt; 0.05 level (ANOVA and LSD).</p>
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<p>Effect of amendment with raw garlic stalk on plant stress indicators. Malondialdehyde (MDA) (<b>A</b>) 1st flowering, (<b>B</b>) 1st fruiting, (<b>C</b>) 2nd flowering and (<b>D</b>) 2nd fruiting, hydrogen peroxide (H<sub>2</sub>O<sub>2</sub>) contents (<b>E</b>) 1st flowering, (<b>F</b>) 1st fruiting, (<b>G</b>) 2nd flowering and (<b>H</b>) 2nd fruiting. The error bars represent standard error of the means (<span class="html-italic">n</span> = 3). Different letters between treatments at different sampling stages show significant difference at <span class="html-italic">p</span> &lt; 0.05 level (ANOVA and LSD).</p>
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<p>Effect of amendment with raw garlic stalk on superoxide dismutase (SOD) activity (<b>A</b>) 1st flowering, (<b>B</b>) 1st fruiting, (<b>C</b>) 2nd flowering and (<b>D</b>) 2nd fruiting. Peroxidase (POD) activity (<b>E</b>) 1st flowering, (<b>F</b>) 1st fruiting, (<b>G</b>) 2nd flowering and (<b>H</b>) 2nd fruiting. Phenylalanine ammonia–lyase (PAL) activity (<b>I</b>) 1st flowering, (<b>J</b>) 1st fruiting, (<b>K</b>) 2nd flowering and (<b>L</b>) 2nd fruiting. Polyphenol oxidase (PPO) activity (<b>M</b>) 1st flowering, (<b>N</b>) 1st fruiting, (<b>O</b>) 2nd flowering and (<b>P</b>) 2nd fruiting. The error bars represent standard error of the means (<span class="html-italic">n</span> = 3). Different letters between treatments at different sampling stages show significant difference at <span class="html-italic">p</span> &lt; 0.05 level (ANOVA and LSD).</p>
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<p>Effect of amendment with raw garlic stalk on fruit yield of eggplant. The error bars represent standard error of the means (<span class="html-italic">n</span> = 3). Different letters indicate different means for each treatment two year combination significant difference at <span class="html-italic">p</span> &lt; 0.05 level (ANOVA and LSD).</p>
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14 pages, 1751 KiB  
Article
Aggressiveness and Fumonisins Production of Fusarium Subglutinans and Fusarium Temperatum on Korean Maize Cultivars
by Setu Bazie Tagele, Sang Woo Kim, Hyun Gu Lee and Youn Su Lee
Agronomy 2019, 9(2), 88; https://doi.org/10.3390/agronomy9020088 - 15 Feb 2019
Cited by 11 | Viewed by 4905
Abstract
Fusarium root rot and stalk rot are becoming a threat to maize production worldwide. However, there is still limited information about the aggressiveness of Fusarium subglutinans Edwards and Fusarium temperatum and their relationship with fumonisin production. In this study, for the first time, [...] Read more.
Fusarium root rot and stalk rot are becoming a threat to maize production worldwide. However, there is still limited information about the aggressiveness of Fusarium subglutinans Edwards and Fusarium temperatum and their relationship with fumonisin production. In this study, for the first time, the reaction of seven Korean maize cultivars to F. subglutinans and F. temperatum was investigated. The results showed that among the maize cultivars, Hik-chal and Miheung-chal had the highest Fusarium-induced root rot and stalk rot severity, while De Hack-chal had the lowest disease severity regardless of the Fusarium species. Furthermore, the disease resistant cv. De Hack-chal accumulated low levels of fumonisins (FUM) in the infected stalk, while cv. Hik-chal and Miheung-chal had the highest level of FUM. It is worth to note that, plants infected with F. temperatum had a higher FUM concentration compared to cultivars infected with F. subglutinans. The present study shows a significant correlation between stalk rot ratings and FUM levels and it also presents new information about the potential risk of FUM contamination of maize stalk with F. subglutinans and F. temperatum in South Korea. In addition, enzyme activities like polyphenol oxidase (PPO), peroxidase (POD), and the amount of total phenol content (TPC) were studied in selected susceptible cultivar Miheung-chal and resistant cultivar De Hack-chal. The activity of PPO, POD and concentration of TPC were generally higher in the roots of the resistant cultivar than the susceptible cultivar. Moreover, following inoculation of either F. subglutinans or F. temperatum, there was a significant increase in PPO and POD activity in the roots of both cultivars. Hence, the information provided in this study could be helpful to better understand the mechanisms of resistance response to infection of Fusarium root rot pathogens. Full article
(This article belongs to the Special Issue Genetics and Genomics of Disease Resistance in Crops)
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<p>Maize stalk rot of cultivar (<b>A</b>,<b>H</b>) Hik-chal, (<b>B</b>,<b>I</b>) Miheung-chal, (<b>C</b>,<b>J</b>) Mibeak-2ho, (<b>D</b>,<b>K</b>) Cho-dang, (<b>E</b>,<b>L</b>) Chahong-chal, (<b>F</b>,<b>M</b>) Speed De Hack-chal, and (<b>G</b>,<b>N</b>) and De Hack-chal (top, A − G = inoculated with <span class="html-italic">F. subglutinans</span>, bottom, H – N = inoculated with <span class="html-italic">F. temperatum</span>).</p>
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<p>Pictorial view of root inoculated with (<b>A</b>,<b>D</b>) no pathogen, (<b>B</b>,<b>E</b>) <span class="html-italic">F. subglutinans</span>, and (<b>C</b>,<b>F</b>) <span class="html-italic">F. temperatum</span>) (top, <b>A</b>–<b>C</b> = Miheung-chal, bottom, <b>D</b>–<b>F</b> = De Hack-Chal).</p>
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<p>Activity of (<b>A</b>) polyphenol oxidase, (PPO) (<b>B</b>) peroxidase POD and concentration of (<b>C</b>) total phenol content (TPC) of inoculated (<span class="html-italic">F. subglutinans</span> and <span class="html-italic">F. temperatum</span>) and non-inoculated plants of susceptible (Miheung-chal) and resistant (De Hack-chal) cultivars. Mean values having the same letter (s) in each cultivar are not statistically different (<span class="html-italic">p</span> ≤ 0.05).</p>
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21 pages, 549 KiB  
Review
Agronomic Basis and Strategies for Precision Water Management: A Review
by Jasmine Neupane and Wenxuan Guo
Agronomy 2019, 9(2), 87; https://doi.org/10.3390/agronomy9020087 - 14 Feb 2019
Cited by 87 | Viewed by 12206
Abstract
Agriculture faces the challenge of feeding a growing population with limited or depleting fresh water resources. Advances in irrigation systems and technologies allow site-specific application of irrigation water within the field to improve water use efficiency or reduce water usage for sustainable crop [...] Read more.
Agriculture faces the challenge of feeding a growing population with limited or depleting fresh water resources. Advances in irrigation systems and technologies allow site-specific application of irrigation water within the field to improve water use efficiency or reduce water usage for sustainable crop production, especially in arid and semi-arid regions. This paper discusses recent development of variable-rate irrigation (VRI) technologies, data and information for VRI application, and impacts of VRI, including profitability using this technology, with a focus on agronomic factors in precision water management. The development in sprinkler systems enabled irrigation application with greater precision at the scale of individual nozzle control. Further research is required to evaluate VRI prescription maps integrating different soil and crop characteristics in different environments. On-farm trials and whole-field studies are needed to provide support information for practical VRI applications. Future research also needs to address the adjustment of the spatial distribution of prescription zones in response to temporal variability in soil water status and crop growing conditions, which can be evaluated by incorporating remote and proximal sensing data. Comprehensive decision support tools are required to help the user decide where to apply how much irrigation water at different crop growth stages to optimize water use and crop production based on the regional climate conditions and cropping systems. Full article
(This article belongs to the Special Issue Agricultural Water Management)
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<p>Prescription maps with different control scenarios for four irrigation depths ([<a href="#B48-agronomy-09-00087" class="html-bibr">48</a>], reproduced with permission from Springer Nature).</p>
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18 pages, 6661 KiB  
Article
Effect of Large-Scale Cultivated Land Expansion on the Balance of Soil Carbon and Nitrogen in the Tarim Basin
by Erqi Xu, Hongqi Zhang and Yongmei Xu
Agronomy 2019, 9(2), 86; https://doi.org/10.3390/agronomy9020086 - 14 Feb 2019
Cited by 18 | Viewed by 5646
Abstract
Land reclamation influences the soil carbon and nitrogen cycling, but its scale and time effects on the balance of soil carbon and nitrogen are still uncertain. Taking the Tarim Basin as the study area, the impact of land reclamation on the soil organic [...] Read more.
Land reclamation influences the soil carbon and nitrogen cycling, but its scale and time effects on the balance of soil carbon and nitrogen are still uncertain. Taking the Tarim Basin as the study area, the impact of land reclamation on the soil organic carbon (SOC), total nitrogen (TN), and carbon to nitrogen (C:N) ratio was explored by the multiple temporal changes of land use and soil samples. Remote sensing detected that cropland nearly doubled in area from 1978 to 2015. Spatial analysis techniques were used to identify land changes, including the prior land uses and cultivation ages. Using land reclamation history information, a specially designed soil sampling was conducted in 2015 and compared to soil properties in ca. 1978. Results found a decoupling characteristic between the C:N ratio and SOC or TN, indicating that changes in SOC and TN do not correspond directly to changes in the C:N ratio. The land reclamation history coupled with the baseline effect has opposite impacts on the temporal rates of change in SOC, TN and C:N ratios. SOC and TN decreased during the initial stage of conversion to cropland and subsequently recovered with increasing cultivation time. By contrast, the C:N ratio for soils derived from grassland increased at the initial stage but the increase declined when cultivated longer, and the C:N ratio decreased for soils derived from forest and fluctuated with the cultivation time. Lower C:N ratios than the global average and its decreasing trend with increasing reclamation age were found in newly reclaimed croplands from grasslands. Sustainable agricultural management practices are suggested to enhance the accumulation of soil carbon and nitrogen, as well as to increase the C:N ratio to match the nitrogen deposition to a larger carbon sequestration. Full article
(This article belongs to the Special Issue Remote Sensing Applications for Agriculture and Crop Modelling)
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<p>Location and soil sampling sites in the area of the Tarim Basin.</p>
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<p>Conceptual framework for exploring impacts of land reclamation history on soil C and N.</p>
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<p>Land use maps of 1978 and 2015 in typical areas of the Tarim Basin.</p>
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<p>Soil C and N for different prior land uses: (<b>a</b>) Soil organic carbon content in 2015, (<b>b</b>) Total nitrogen content in 2015, (<b>c</b>) Change of soil organic carbon content, (<b>d</b>) Change of total nitrogen content. Note: 1. Group differences after one-way ANOVA (<span class="html-italic">p</span> &lt; 0.05) was indicated by different lowercase letters. 2. Bar length gives the mean value, vertical whisker of bar for each column indicate standard errors of the mean. 3. Line within the boxes gives the median value, box means the 25th and 75th percentile, whisker of box represents 1.5 times the length of the box from either end of the box (1.5 times the interquartile range), circle represents outliers and extremes. The same as below.</p>
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<p>Impact of reclamation ages on soil C and N: (<b>a</b>) Soil organic carbon content in 2015 assarted from grassland, (<b>b</b>) Total nitrogen content in 2015 assarted from grassland, (<b>c</b>) Soil organic carbon content in 2015 assarted from forest, (<b>d</b>) Total nitrogen content in 2015 assarted from forest, (<b>e</b>) Change of soil organic carbon content assarted from grassland, (<b>f</b>) Change of total nitrogen content assarted from grassland, (<b>g</b>) Change of soil organic carbon content assarted from forest, (<b>h</b>) Change of total nitrogen content assarted from forest. NEW = newly assarted (1–5 years), YOU = young assarted (6–15 years), MED = medium assarted (16–25 years), OLD = old assarted (26–37 years), REF = referenced cropland assarted before 1978.</p>
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<p>Soil C:N ratio for different prior land uses: (<b>a</b>) Values in 2015, (<b>b</b>) Changes from ca.1978 to 2015.</p>
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<p>Impact of reclamation ages on soil C:N ratio: (<b>a</b>) Values in 2015 assarted from grassland, (<b>b</b>) values in 2015 assarted from forest, (<b>c</b>) Changes assarted from grassland, (<b>d</b>) Changes assarted from forest. NEW = newly assarted (1–5 years), YOU = young assarted (6–15 years), MED = medium assarted (16–25 years), OLD = old assarted (26–37 years), REF = referenced cropland assarted before 1978. NEW = newly assarted (1–5 years), YOU = young assarted (6–15 years), MED = medium assarted (16–25 years), OLD = old assarted (26–37 years), REF = referenced cropland assarted before 1978.</p>
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13 pages, 1141 KiB  
Article
Agroecological Practices and Agrobiodiversity: A Case Study on Organic Orange in Southern Italy
by Corrado Ciaccia, Anna La Torre, Filippo Ferlito, Elena Testani, Valerio Battaglia, Luca Salvati and Giancarlo Roccuzzo
Agronomy 2019, 9(2), 85; https://doi.org/10.3390/agronomy9020085 - 14 Feb 2019
Cited by 19 | Viewed by 4671
Abstract
The integration of Agroecological Service Crops (ASCs) into agroecosystems can provide several ecological services, such as nutrient cycling and disease and weed management. A two-year experiment on an organic orchard was carried out to compare barley (B) and horse bean (HB) ASCs with [...] Read more.
The integration of Agroecological Service Crops (ASCs) into agroecosystems can provide several ecological services, such as nutrient cycling and disease and weed management. A two-year experiment on an organic orchard was carried out to compare barley (B) and horse bean (HB) ASCs with a control without ASC (Cont) in combination with fertilizers. Their effects on soil fertility and weed- and soil-borne fungi communities were evaluated by direct measurements, visual estimation, and indicators computation. A Principal Component Analysis (PCA) was used to identify latent patterns and redundancy among variables, whereas a correlation analysis was used to discriminate the compared systems within the PCA matrix. The empirical results of this study put in evidence the correlation among soil, weed, and fungal variables. A slight contribution of fertilizers on the system’s variability was observed, whereas a clear effect of ASCs was highlighted. The systems differed in weed communities, with the lowest density associated to B and the highest to Cont. B showed the highest fungal diversity, with changes in community compared to HB. HB showed a contribution on soil fertility, being associated to organic matter increase and N availability, and evidencing mixed impacts on soil quality and ecosystem functioning. Overall, the above-ground diversity and below-ground community results were inter-correlated. Full article
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<p>Mean monthly temperature and rainfall at the ‘Long term trial on organic Citrus’–PALAP9 during January 2014–December 2015 compared with long-term (30 years.) mean values.</p>
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<p>Principal Component Analysis (PCA) ordination diagram (biplot) depicting the localization of the studied samples from the experimental trial over the two years of the experiment in relation to the soil fertility parameters, weed species percentage, weed biodiversity indices and fungi and oomycetes genera percentage and biodiversity indices. The first axis accounts for 10.0% of the total variation of the dataset. Together, the first two axes explain 16.6% of the variability in the dataset. The direction and length of the arrows indicate the direction and magnitude in which each variable contributes to the configuration of the point, respectively. The angle between each arrow and the axes is inversely proportional to the correlation between each variable and the axes constructed in the ordination.</p>
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<p>Weed species relative density in each season over the two-year experiment (<b>a</b>) and in each treatment for each year (<b>b</b>). AMARE: <span class="html-italic">Amaranthus retroflexus</span> L.; BRSNI: <span class="html-italic">Brassica nigra</span> (L.) Koch, CAPBP: <span class="html-italic">Capsella borsa</span>-<span class="html-italic">pastoris</span> (L.) Medik., CYPRO: <span class="html-italic">Cyperus rotondus</span> L.; DIPER: <span class="html-italic">Diplotaxis erucoides</span> (L.) DC.; FUMOF: <span class="html-italic">Fumaria officinalis</span> L.; LAMAM: <span class="html-italic">Lamium amplexicaule</span> L.; MALSI: <span class="html-italic">Malva sylvestris</span> L.; POROL: <span class="html-italic">Portulaca oleracea</span> L.; <span class="html-italic">Sonchus</span> spp.: <span class="html-italic">S</span>. <span class="html-italic">arvensis</span> L., <span class="html-italic">S</span>. <span class="html-italic">asper</span> (L.) Hill, <span class="html-italic">S. oleraceus</span> L.; STEME: <span class="html-italic">Stellaria media</span> (L.) Vill., URTUR: <span class="html-italic">Urtica urens</span> L.</p>
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14 pages, 1875 KiB  
Article
An Automatic Non-Destructive Method for the Classification of the Ripeness Stage of Red Delicious Apples in Orchards Using Aerial Video
by Sajad Sabzi, Yousef Abbaspour-Gilandeh, Ginés García-Mateos, Antonio Ruiz-Canales, José Miguel Molina-Martínez and Juan Ignacio Arribas
Agronomy 2019, 9(2), 84; https://doi.org/10.3390/agronomy9020084 - 14 Feb 2019
Cited by 28 | Viewed by 5607
Abstract
The estimation of the ripening state in orchards helps improve post-harvest processes. Picking fruits based on their stage of maturity can reduce the cost of storage and increase market outcomes. Moreover, aerial images and the estimated ripeness can be used as indicators for [...] Read more.
The estimation of the ripening state in orchards helps improve post-harvest processes. Picking fruits based on their stage of maturity can reduce the cost of storage and increase market outcomes. Moreover, aerial images and the estimated ripeness can be used as indicators for detecting water stress and determining the water applied during irrigation. Additionally, they can also be related to the crop coefficient (Kc) of seasonal water needs. The purpose of this research is to develop a new computer vision algorithm to detect the existing fruits in aerial images of an apple cultivar (of Red Delicious variety) and estimate their ripeness stage among four possible classes: unripe, half-ripe, ripe, and overripe. The proposed method is based on a combination of the most effective color features and a classifier based on artificial neural networks optimized with genetic algorithms. The obtained results indicate an average classification accuracy of 97.88%, over a dataset of 8390 images and 27,687 apples, and values of the area under the ROC (receiver operating characteristic) curve near or above 0.99 for all classes. We believe this is a remarkable performance that allows a proper non-intrusive estimation of ripening that will help to improve harvesting strategies. Full article
(This article belongs to the Section Innovative Cropping Systems)
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<p>Scheme of the proposed algorithm for apple ripening estimation using color features.</p>
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<p>Sample images of the four different apple ripening stages (<span class="html-italic">Malus Domestica</span> L., var. Red Delicious) extracted from the captured frames under natural lighting conditions. (<b>a</b>) Unripe. (<b>b</b>) Half-ripe. (<b>c</b>) Ripe. (<b>d</b>) Overripe.</p>
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<p>Example of the steps in apple segmentation. (<b>a</b>) Original video frame. (<b>b</b>) First step of segmentation based on color thresholding in the L*u*v* color space. (<b>c</b>) Second step, based on texture features. (<b>d</b>) Third step, based on intensity transformation. (<b>e</b>) Final step, based on thresholding in RGB color space.</p>
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<p>ROC curves of the proposed hybrid ANN-GA classifier.</p>
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<p>A sample video frame after application of the proposed apple ripening classification. The number corresponds to the ripening label (1. unripe; 2. half-ripe). A sample video with more results is available at <a href="https://youtu.be/A3ROtqRy9os" target="_blank">https://youtu.be/A3ROtqRy9os</a>.</p>
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16 pages, 1867 KiB  
Article
Research into the E-Learning Model of Agriculture Technology Companies: Analysis by Deep Learning
by Chi-Hsuan Lin, Wei-Chuan Wang, Chun-Yung Liu, Po-Nien Pan and Hou-Ru Pan
Agronomy 2019, 9(2), 83; https://doi.org/10.3390/agronomy9020083 - 13 Feb 2019
Cited by 10 | Viewed by 5103
Abstract
With the advancement of technology, the traditional e-learning model may expand the realm of knowledge and differentiate learning by means of deep learning (DL) and augmented reality (AR) scenarios. These scenarios make use of interactive interfaces that incorporate various operating methods, angles, perceptions, [...] Read more.
With the advancement of technology, the traditional e-learning model may expand the realm of knowledge and differentiate learning by means of deep learning (DL) and augmented reality (AR) scenarios. These scenarios make use of interactive interfaces that incorporate various operating methods, angles, perceptions, and experiences, and also draw on multimedia content and active interactive models. Modern education emphasizes that learning should occur in the process of constructing knowledge scenarios and should proceed through learning scenarios and activities. Compared to traditional “spoon-feeding” education, the model learning scenario is initiated with the learner at the center, allowing the person involved in the learning activity to solve problems and further develop their individual capabilities through exploring, thinking and a series of interactions and feedback. This study examined how students in the agriculture technological industry make use of AR digital learning to develop their industry-related knowledge and techniques to become stronger and more mature so that they unconsciously apply these techniques as employees, as well as encouraging innovative thought and methods to create new value for the enterprise. Full article
(This article belongs to the Special Issue Deep Learning Techniques for Agronomy Applications)
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<p>Reality–Virtuality (RV) Continuum. Source: Milgram, et al., (1994) SPIE. Kyoto, Japan: 283 [<a href="#B7-agronomy-09-00083" class="html-bibr">7</a>].</p>
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<p>D&amp;M information system success model (D&amp;M I/S success model). Source: W. H. DeLone and E. R. McLean [<a href="#B14-agronomy-09-00083" class="html-bibr">14</a>], Journal of Management Information System.</p>
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<p>Expectation confirmation theory (ECT) [<a href="#B15-agronomy-09-00083" class="html-bibr">15</a>].</p>
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<p>Study framework. Source: Authors’ projection in Partial Least Squares (PLS).</p>
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<p>Structural model relation route qualification results. Source: Author’s projection in PLS.</p>
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16 pages, 1208 KiB  
Review
Reframing the Debate Surrounding the Yield Gap between Organic and Conventional Farming
by Klaus-Peter Wilbois and Jennifer Elise Schmidt
Agronomy 2019, 9(2), 82; https://doi.org/10.3390/agronomy9020082 - 13 Feb 2019
Cited by 44 | Viewed by 12955
Abstract
In this article, we review the literature regarding the yield gap between organic and conventional agriculture and then reflect on the corresponding debate on whether or not organic farming can feed the world. We analyze the current framework and highlight the need to [...] Read more.
In this article, we review the literature regarding the yield gap between organic and conventional agriculture and then reflect on the corresponding debate on whether or not organic farming can feed the world. We analyze the current framework and highlight the need to reframe the yield gap debate away from “Can organic feed the world?” towards the more pragmatic question, “How can organic agriculture contribute to feeding the world?”. Furthermore, we challenge the benchmarks that are used in present yield comparison studies, as they are based on fundamentally distinct paradigms of the respective farming methods, and then come up with a novel model to better understand the nature of yield gaps and the benchmarks that they are premised on. We thus conclude that, by establishing appropriate benchmarks, re-prioritizing research needs, and focusing on transforming natural resources rather than inputs, organic systems can raise their yields and play an ever-greater role in global sustainable agriculture and food production in the future. Full article
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<p>Simplified model to describe a cropping system as a process of transformation.</p>
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<p>The true yield gap between organic and intensive conventional management shrinks when an ecologically sustainable threshold is set as benchmark.</p>
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<p>The gap between conventional and organic yield may go into reverse under less favorable conditions.</p>
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11 pages, 773 KiB  
Article
Screening Tolerance to Phosphorus Deficiency and Validation of Phosphorus Uptake 1 (Pup1) Gene-Linked Markers in Thai Indigenous Upland Rice Germplasm
by Sompong Chankaew, Tidarat Monkham, Wanwipa Pinta, Jirawat Sanitchon, Wanwipa Kaewpradit and Peerasak Srinives
Agronomy 2019, 9(2), 81; https://doi.org/10.3390/agronomy9020081 - 12 Feb 2019
Cited by 9 | Viewed by 4489
Abstract
Phosphorus (P) deficiency is a major factor limiting rice yield throughout the world. Fortunately, some rice accessions are tolerant and can thrive well, even in soils with low P content. The ability to uptake P is heritable, and thus can be incorporated into [...] Read more.
Phosphorus (P) deficiency is a major factor limiting rice yield throughout the world. Fortunately, some rice accessions are tolerant and can thrive well, even in soils with low P content. The ability to uptake P is heritable, and thus can be incorporated into rice cultivars through standard breeding methods. The objective of this study was to screen for tolerance to phosphorus deficiency and validate the tolerant accessions with phosphorus uptake 1 (Pup1) gene-linked markers in Thai indigenous upland rice germplasm. One hundred sixty-eight rice varieties were screened in a solution culture and assigned a phosphorus deficiency tolerance index and plant symptom score. Eleven upland rice accessions (ULR026, ULR031, ULR124, ULR145, ULR180, ULR183, ULR185, ULR186, ULR213, ULR260, and ULR305), together with the lowland rice cultivar (PLD), were classified as tolerant. They were each validated by nine markers linked to the Pup1 locus and observed for the expected polymerase chain reaction (PCR) product of 0 to 9 markers. The presence or absence of the tolerant allele at the Pup1 locus showed only a slight relationship with the tolerance. Moreover, some lines such as ULR183 and ULR213 expressed high tolerance without the Pup1-linked gene product. Both accessions are useful for the exploration of novel genes conferring tolerance to phosphorus deficiency. Full article
(This article belongs to the Special Issue Soil Phosphorus Dynamics: Agronomic and Environmental Impacts)
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Figure 1
<p>Frequency distribution of the phosphorus deficiency tolerance index (PDTI) of 168 Thai indigenous upland rice cultivars in Season 1, Season 2, and in combination.</p>
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<p>Mean and standard deviation of five traits related to Phosphorus deficiency tolerance index (PDTI) in three genotypic groups under +P and −P. Groups 1, 2, and 3 represent high, moderate, and low tolerances under phosphorus deficiency. Number of rice accessions in Groups 1, 2, and 3 are 22, 107, and 37, respectively. (<b>a</b>) the tiller number under +P and −P of three genotypes groups, (<b>b</b>) total dry weight per plant of three genotypes group under +P and −P, (<b>c</b>) the root dry weight per plant of three genotypes groups under +P and −P, (<b>d</b>) the shoot dry weight of each genotypes group under +P and −P and (<b>e</b>) is the root/shoot ratio of each genotypes groups under +P and −P.</p>
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<p>The relationship between plant symptom score (SC) and phosphorus deficiency tolerance index (PDTI) of 168 indigenous upland rice genotypes; red = Group 1, blue = Group 2, and black = Group 3.</p>
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