118,219 research outputs found
An Efficient Method for Establishing Canopy Photosynthesis Curves of Lettuce (Lactuca sativa L.) with Light Intensity and CO2 Concentration Variables Using Controlled Growth Chamber
No full textFor developing a canopy photosynthesis model, an efficient method to measure the photosynthetic rate in a growth chamber is required. The objective of this study was to develop a method for establishing canopy photosynthetic rate curves of romaine lettuce (Lactuca sativa L.) with light intensity and CO2 concentration variables using controlled growth chamber. The plants were grown in plant factory modules, and the canopy photosynthesis rates were measured in sealed growth chambers made of acrylic (1.0 x 0.8 x 0.5 m). First, the canopy photosynthetic rates of the plants were measured, and then the time constants were compared between two application methods: 1) changing light intensity (340, 270, 200, and 130 μmol·m-2·s-1) at a fixed CO2 concentration (1,000 μmol·mol-1) and 2) changing CO2 concentration (600, 1,000, 1,400, and 1,800 μmol·mol-1) at a fixed light intensity (200 μmol·m-2·s-1). Second, the canopy photosynthetic rates were measured by changing the light intensity at a CO2 concentration of 1,000 μmol·mol-1 and compared with those measured by changing the CO2 concentration at a light intensity of 200 μmol·m-2·s-1. The time constant when changing the CO2 concentration at the fixed light intensity was 3.2 times longer, and the deviation in photosynthetic rate was larger than when changing the light intensity. The canopy photosynthetic rate was obtained stably with a time lag of one min when changing the light intensity, while a time lag of six min or longer was required when changing the CO2 concentration. Therefore, changing the light intensity at a fixed CO2 concentration is more appropriate for short-term measurement of canopy photosynthesis using a growth chamber.N
Optimal Duration of Drought Stress Near Harvest for Promoting Bioactive Compounds and Antioxidant Capacity in Kale with or without UV-B Radiation in Plant Factories
No full textAmong abiotic stresses, both drought and UV-B radiation effectively trigger the accumulation of secondary metabolites, and can be widely applied in plant factories. The objectives of this study were to investigate antioxidant accumulation under drought stress alone, or in combination with UV-B radiation near harvest, and to determine an optimal treatment time for maximum antioxidant production. Kale (Brassica oleracea L. var. acephala) plants were grown in a plant factory and harvested at 42 days after transplanting. The single and combination treatments lasted for 7 to 1 days and 4 to 2 days before harvest, respectively. The results of both Fv/Fm (maximal photochemical efficiency in photosystem II) and leaf water potential could ensure the function of photosynthesis and maintain normal leaf moisture in single drought treatments of less than 4 days. The total phenolic and flavonoid contents and antioxidant activities were significantly increased in both single and combination treatments for 3 to 4 days, compared to other treatments. The supplementary UV-B treatments showed no extra formation of antioxidants compared to the single drought treatments. As a result, drought for 3 days before harvest could achieve the highest potential value of kale as a source of natural antioxidants
Forecasting root-zone electrical conductivity of nutrient solutions in closed-loop soilless cultures via a recurrent neural network using environmental and cultivation information
No full textIn existing closed-loop soilless cultures, nutrient solutions are controlled by the electrical conductivity (EC) of the solution. However, the EC of nutrient solutions is affected by both growth environments and crop growth, so it is hard to predict the EC of nutrient solution. The objective of this study was to predict the EC of root-zone nutrient solutions in closed-loop soilless cultures using recurrent neural network (RNN). In a test greenhouse with sweet peppers (Capsicum annuum L.), data were measured every 10 s from October 15 to December 31, 2014. Mean values for every hour were analyzed. Validation accuracy (R-2) of a single-layer long short-term memory (LSTM) was 0.92 and root-mean-square error (RMSE) was 0.07, which were the best results among the different RNNs. The trained LSTM predicted the substrate EC accurately at all ranges. Test accuracy (R-2) was 0.72 and RMSE was 0.08, which were lower than values for the validation. Deep learning algorithms were more accurate when more data were added for training. The addition of other environmental factors or plant growth data would improve model robustness. A trained LSTM can control the nutrient solutions in closedloop soilless cultures based on predicted future EC. Therefore, the algorithm can make a planned management of nutrient solutions possible, reducing resource waste.OAIID:RECH_ACHV_DSTSH_NO:T201815032RECH_ACHV_FG:RR00200001ADJUST_YN:EMP_ID:A002344CITE_RATE:3.678DEPT_NM:식물생산과학부EMAIL:[email protected]_YN:YY
Going Beyond Counting First Authors in Author Co-citation Analysis
No full textThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Square Dancing with the Stars to Enhance Dynamic Hirschman Linkages?
No full textIn this Presidential Address, the author takes the reader on a reconnaissance of his life and time as a regional scientist. He points out scenery he found scintillating along the way, hoping that some may pick up the banner and chew on a few of the ideas for a while. He suggests a revisit to Albert O. Hirschman’s notion of key sectors and more empirical analysis related to Marcus Berliant’s and Masahisa Fujita’s notion of knowledge creation and transfer.Presidential Address, San Antonio, Texas, March 29, 2014 (53rd Meetings of the Southern Regional Science Association
Appropriate Similarity Measures for Author Cocitation Analysis
No full textWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Spectral dependence of electrical energy-based photosynthetic efficiency at single leaf and canopy levels in green- and red-leaf lettuces
No full textThe spectrum of light affects both the electrical energy consumption by plants and photosynthetic efficiency. In a plant factory, where light-emitting diodes (LEDs) serve as an alternative to solar light, the optimal spectrum of light should be carefully chosen to maximize the rate of photosynthesis and the electrical energy efficiency of the crop. The objectives of this study were to investigate the photosynthetic rate of different colored lettuces (reddish and green leaves), to quantify the spectral dependence of photosynthetic efficiency, and to optimize the LED spectrum for maximum canopy photosynthesis and electrical energy consumption in lettuce grown in a plant factory. Two lettuce cultivars (Lactuca sativa L.), 'JeokChukMyeon' and 'CheongChukMyeon', were assessed for light absorption and photosynthetic efficiency at the single leaf and canopy levels, and the relative consumption of electrical energy from the LED lights was measured at 18 narrow wavelength bands of 10 nm from 400 to 700 nm. Anthocyanin and chlorophyll content (SPAD value) were measured and correlated with leaf color. Light interception by the canopy was estimated with light transmittance models. The light absorption was similar among the green and reddish lettuce cultivars at most wavelengths, but slightly higher in the reddish leaves around 550 nm (green region). In the reddish leaves, photosynthetic rates per incident photon of a single leaf had two peaks at 650-660 and 400-410 nm, while the photosynthetic rate per absorbed photon had three peaks at 650-660, 400-410, and 540-560 nm. In the green region of the light spectrum, both photosynthetic rates per incident photon and those per absorbed photon were lower in the reddish cultivars than in the green cultivars. The spectral dependence of light absorption at the canopy level was much weaker than that at the single leaf level. The quantum yield and absorption of green light at the canopy level were nearly same as those of blue and red lights, indicating that the photosynthetic efficiency of green light at the canopy level was higher than that at the single leaf level. The relative electrical energy consumption was lower in the green region than in the red and blue regions. Therefore, the photosynthetic efficiency based on electrical energy consumption at the canopy level was much lower with green LEDs than with blue or red LEDs. These results describe the plant response to the light spectrum at the canopy level and can be useful for optimizing artificial lighting sources for maximum plant productivity and energy-savings in a plant factory.OAIID:RECH_ACHV_DSTSH_NO:T201713651RECH_ACHV_FG:RR00200001ADJUST_YN:EMP_ID:A002344CITE_RATE:.812DEPT_NM:식물생산과학부EMAIL:[email protected]_YN:YN
Letter from unknown writer to Jesse L. Boyce
No full textLetter to Jesse L. Boyce from unknown author (possibly Jack) about the investigation into the powder magazine located in the Grand Canyon. Some personal news is included in the letter such as the writer's marriage to the daughter of C.A. Taylor, former Supervisor of Cochise County
Estimating the leaf area index of bell peppers according to growth stage using ray-tracing simulation and a long short-term memory algorithm (vol 13, pg 548, 2020)
No full textThe leaf area index (LAI), which represents crop growth characteristics, is used to calculate canopy photosynthetic rates, set irrigation standards, and predict crop growth. The LAI can be non-destructively and continuously estimated using the light-intensity ratio of the upper and lower crop canopy, but it is affected by solar altitude and external weather conditions. The objective of this study was to develop a method to estimate the LAI of bell peppers (Capsicum annuum L.) using the light-intensity ratio of the upper and lower crop canopy via solar altitude and weather conditions. Growth stages and weather conditions with solar altitude were set using 3D-scanned plant models and ray-tracing simulation, respectively. The light intensities at each location of the canopy for given conditions were calculated using ray-tracing simulation. The relationship between the light-intensity ratio and the LAI was analyzed using a long short-term memory (LSTM) algorithm, which is a type of artificial neural network. According to our results, the ratio varied depending on solar altitude and external weather conditions and exponentially decreased with increasing LAI. This LSTM algorithmic approach was able to quantitatively analyze this complex relationship; compared with a greenhouse experiment for validation, the algorithm was highly accurate (R2 = 0.808). Accuracy further increased when solar altitude and weather conditions were added to the model. Therefore, we conclude that, using this method, the LAI can be accurately measured in a non-destructive and continuous manner.Y
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