• KOREAN JOURNAL OF CROP SCIENCE
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• v.57 no.1
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• pp.89-97
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• 2012

Reduction of greenhouse gas (GHG) emissions from upland crop field as well as paddy field is being required, but little information on GHG emissions according to cultivation practices in upland field is available. Soil GHG emissions during the growing season were investigated in the field of three decades rotation and tillage treatments which were consisted of plow, chiesl tillage and no tillage in west central Indiana, USA in 2006. Seasonal cumulative $CO_2$ emissions were not different among treatments. $CH_4$ emission increased a little in plow tillage during early soybean growing season. Most of $N_2O$ emission occurred during early corn growing season after N-fertilizer application from mid June to mid July, and was significantly affected by tillage practices in which seasonal cumulative $N_2O$ emission was significantly higher under chisel tillage. $N_2O$ emission under no-tillage was lower about 64% and 39% than that under chisel tillage and plow tillage, respectively. No-tillage practice with rotation of corn and soybean seems to be promising in point of less GHG emission and less labor for cultivation without grain yield reduction.

• Proceedings of the Korean Society of Crop Science Conference
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• 2017.06a
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• pp.107-107
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• 2017

Sweet corns are enjoyed worldwide as processed products and fresh ears. Types of sweet corn are based on the gene(s) involved. The oldest sweet corn type has a gene called "sugary (su)". Sugary-based sweet corn was typically named "sweet corn". With its relatively short shelf life and the discovery of a complementary gene, "sugary enhanced (se)", the sweet corn (su only) was rapidly replaced with another type of sweet corns, sugary enhanced sweet corn, which has recessive homozygous su/su, se/se genotype. With the incorporation of se/se genotype into existing su/su genotype, sugary enhanced sweet corn has better shelf life and increased sweetness while maintaining its creamy texture due to high level of water soluble polysaccharide, phytoglycogen. Super sweet corn as the name implies has higher level of sweetness and better shelf life than sugary enhanced sweet corn due to "shrunken2 (sh2)" gene although there's no creamy texture of su-based sweet corns. Distinction between sh2/sh2 and su/su genotypes in seeds is phenotypically possible. The Involvement of se/se genotype under su/su genotype, however, is visually impossible. The genotype sh2/sh2 is also phenotypically epistatic to su/su genotype when both genotypes are present in an individual, meaning the seed shape for double recessive sh2/sh2 su/su genotype is much the same as sh2/sh2 +/+ genotype. Hence, identifying the double and triple recessive homozygous genotypes from su, se and sh2 genes involves a testcross to single recessive genotype, chemical analysis or DNA-based marker development. For these reasons, sweetcorn breeders were hastened to put them together into one cultivar. This, however, appears to be no longer the case. Sweet corn companies began to sell their sweet corn hybrids with different combinations of abovementioned three genes under a few different trademarks or genetic codes, i.g. Sweet $Breed^{TM}$, Sweet $Gene^{TM}$, Synergistic corn, Augmented Supersweet corn. A total of 49 commercial sweet corn F1 hybrids with B73 as a check were genotyped using DNA-based markers. The genotype of field corn inbred B73 was +/+ +/+ +/+ for su, se and sh2 as expected. All twelve sugary enhanced sweet corn hybrids had the genotype of su/su se/se +/+. Of sixteen synergistic hybrids, thirteen cultivars had su/su se/se sh2/+ genotype while the genotype of two hybrids and the remaining one hybrid was su/su se/+ sh2/+, and su/su +/+ sh2/+, respectively. The synergistic hybrids all were recessive homozygous for su gene and heterozygous for sh2 gene. Among the fifteen augmented supersweet hybrids, only one hybrid was triple recessive homozygous (su/su se/se sh2/sh2). All the other hybrids had su/su se/+ sh2/sh2 for one hybrid, su/su +/+ sh2/sh2 for three hybrids, su/+ se/se sh2/sh2 for three hybrids, su/+ se/+ sh2/sh2 for four hybrids, and su/+ +/+ sh2/sh2 for three hybrids, respectively. What was believed to be a classic super sweet corn hybrids also had various genotypic combination. There were only two hybrids that turned out to be single recessive sh2 homozygous (+/+ +/+ sh2/sh2) while all the other five hybrids could be classified as one of augmented supersweet genotypes. Implication of the results for extension service and sweet corn breeding will be discussed.

• Journal of The Korean Society of Grassland and Forage Science
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• v.32 no.4
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• pp.369-378
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• 2012

This study was performed out to investigate the influence of the mixed application of chemical fertilizer (CF) and liquid swine manure (LSM) on the growth characteristics, dry matter yield, amino acids, minerals, and free sugars in cultivating silage corn on paddy soils. The field experiment was designed in a randomized block design of 3 repetitions with CF 100% treatment (C), CF 70% + LSM 30% treatment (T1), CF 50% + LSM 50% treatment (T2), CF 30% + LSM 70% treatment (T3), and LSM 100% treatment (T4). At this time, the application of LSM was based solely on the nitrogen. Ear length, ear circle, stem diameter, and stem hardness of the silage corn did not show significant differences between treatments. Fresh yield, dry matter yield and TDN yield were highest in T3, whereas the lowest in C treatment (p<0.05). Crude protein, crude fat, and crude ash content were significantly higher in T1, C, and T4 treatment, respectively (p<0.05). However, NDF, ADF and crude fiber content did not show significant difference between treatments. The total mineral content decreased significantly (p<0.05) as the LSM application rate increased. Total composition amino acid content was higher in the order of T1 > T2 > C > T4 > T3 treatment (p<0.05). Free sugar content was higher in the order of T1 > T3 > T4 > T2 > C treatment (p<0.05). Based on the above results, suggests that the mixed application of chemical fertilizer 30~50% and LSM 50~70% (T2 and T3) is the most effective, considering the yield performance and the content of sugar degree and free sugar affecting silage.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.55 no.2
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• pp.91-97
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• 2010

The growth, yield and chemical properties of waxy corn $Sonjajang{\times}KNU-7$ and $Asan{\times}KNU-7$ planted in different dates and mulch types in a converted paddy field was investigated. Experiment was carried out in a randomized complete block design in a split split-plot arrangement with four replications. Planting dates (D) [May 16 (D1, early), June 1 (D2, middle), June 6 (D3, late)] represented main plots, plastic mulch (M) [(BM, black mulch; TM, transparent mulch)] for subplots while waxy corn crosses [$Sonjajang{\times}KNU-7$ (‘Sonja’) and $Asan{\times}KNU-7$ (‘Asan’)] for sub-subplots. Results showed that D had a significant effect on growth characters except emergence, ear quality except ear diameter, and yield whereas M showed significant effect on growth characters only. Superior growth and ear quality performance were recorded in D1 and BM. In terms of crosses, ‘Sonja’ had better growth performance than ‘Asan’ regardless of D and M, but performed better at D1 and BM. Highest yield was obtained in D1 for BM (2,131 kg $10a^{-1}$) and TM (1,655 kg $10a^{-1}$) but no significant difference in the yield across V was recorded. In terms of starch and sugar contents, a decreasing trend was observed from D1 to D3 regardless of M and V.

• Korean Journal of Remote Sensing
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• v.22 no.3
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• pp.183-197
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• 2006

Combinations of visible and near-infrared (NIR) bands in an image are widely used for estimating vegetation vigor and productivity. Using this approach to understand within-field grain crop variability could allow pre-harvest estimates of yield, and might enable mapping of yield variations without use of a combine yield monitor. The objective of this study was to estimate within-field variations in crop yield using vegetation indices derived from hyperspectral images. Hyperspectral images were acquired using an aerial sensor on multiple dates during the 2003 and 2004 cropping seasons for corn and soybean fields in central Missouri. Vegetation indices, including intensity normalized red (NR), intensity normalized green (NG), normalized difference vegetation index (NDVI), green NDVI (gNDVI), and soil-adjusted vegetation index (SAVI), were derived from the images using wavelengths from 440 nm to 850 nm, with bands selected using an iterative procedure. Accuracy of yield estimation models based on these vegetation indices was assessed by comparison with combine yield monitor data. In 2003, late-season NG provided the best estimation of both corn $(r^2\;=\;0.632)$ and soybean $(r^2\;=\;0.467)$ yields. Stepwise multiple linear regression using multiple hyperspectral bands was also used to estimate yield, and explained similar amounts of yield variation. Corn yield variability was better modeled than was soybean yield variability. Remote sensing was better able to estimate yields in the 2003 season when crop growth was limited by water availability, especially on drought-prone portions of the fields. In 2004, when timely rains during the growing season provided adequate moisture across entire fields and yield variability was less, remote sensing estimates of yield were much poorer $(r^2<0.3)$.

• Journal of Animal Science and Technology
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• v.61 no.4
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• pp.185-191
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• 2019

The aim of this study was to evaluate different herbicides for optimum growth, yield and nutritive value of corn-soybean mixed forage under randomized complete block design. The experimental site was selected and divided equally into 3 blocks. Each block was further divided into 5 plots that each plot had 15 square meter space ($3{\times}5$). Five herbicidal treatments were randomly applied over 5 plots and herbicides were used under 5 herbicidal treatments, viz. 1) No herbicide (control); 2) Pendimethalin; 3) Linuron; 4) S-metolachlor and 5) Ethalfluralin. The collected data were analyzed using ANOVA through SAS 9.1.3 software. The results indicated that growth characteristics were not influenced (p > 0.05) by any herbicide. However, arithmetically corn stalk height was highest in the field of Pendimethalin treatment, whereas highest soybean height was found in the field of S-metolachlor. Arithmetically dry matter (DM) yield was increased with herbicidal treatments as compared to that of control treatment. Relatively highest DM yield (130%) was recorded in the treatment of Ethalfluralin followed by Pendimethalin (126%), S-metolachlor (126%) and Linuron (108%) as compared to that of control treatment. The weed emergence was significantly reduced in all herbicidal treatments as compared to that of control (p > 0.05), but the difference among herbicidal treatments was non-significant. It was concluded that weed emergence can be effectively controlled by use of any tested herbicide. However, optimum DM yield can be achieved through using herbicides; Ethalfluralin, Pendimethalin and S-metolachlor.

• Journal of The Korean Society of Grassland and Forage Science
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• v.19 no.2
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• pp.177-182
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• 1999

This research was carried out in a farm situated in Malkara District of Tekirdag Province (Turkey) in 1994-95, and the effect of genotype and environmental conditions on some characters and variations of these characters in terms of silage yield in 8 dent corn varieties were determined. The results of this research showed that leaf weight, stem diameter, and silage yield had a low broad sense heritability while the number of leaves per plant had a high heritability. Yield performance of varieties varied to a significant degree because of variations in rainfall rate during the growing periods. Leaf number, silage yield, leaf weight, stem diameter, plant height and ear weight varied between 13.33-17.33 number, 8,443-11,114 ton/hec, 152.8-244.2 g, 2,615-2,965 cm, 216.5-252.5 g and 176.2-285.8 g, respectively.

• Agricultural and Biosystems Engineering
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• v.1 no.2
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• pp.88-94
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• 2000

Nitrogen (N) management is critical for corn production. On the other hand, N leaching into the groundwater creates serious environmental problems. There is a demand for sensors that can assess the plant N deficiency throughout the growing season to allow producers to reach their production goals, while maintaining environmental quality. This paper reports on the performance of a vision-based reflectance sensor for real-time assessment of N stress level of corn crops. Data were collected representing the changes in crop reflectance in various spectral ranges over several stages of development in the growing season. The performance of this non-contact sensor was validated under various field conditions with reference measurement from a Minolta SPAD meter and stepped nitrogen treatments.

• Journal of Biosystems Engineering
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• v.42 no.2
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• pp.98-103
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• 2017

Purpose: Precision agriculture includes determining the right amount of nitrogen for a specific location in the field. This work focused on developing and validating a model using variable rate nitrogen application based on the estimated SPAD value from the ground-based image sensor. Methods: A variable rate N application based on the decision making system was performed using a sensor-based variable rate nitrogen application system. To validate the nitrogen application decision making system based on the SPAD values, the developed N recommendation was compared with another conventional N recommendation. Results: Sensor-based variable rate nitrogen application was performed. The nitrogen deficiency level was measured using the image sensor system. Then, a variable rate application was run using the decision model and real-ti me control. Conclusions: These results would be useful for nitrogen management of corn in the field. The developed nitrogen application decision making system worked well, when considering the SPAD value estimation.

• Asian Journal of Turfgrass Science
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• v.3 no.1
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• pp.13-15
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• 1989

In oder to find out the effective seed germination testing method to the field emergence, an experiment was conducted ; 1. TTC testing results were higher percentage than any other germination testing methods . 2. On the corn seed , field emergence was highly correlated with germinator test, TTC test and AA test hut cold test was lower percentage . 3. Field emergence , on the soybean seeds was highly correlated with AA test and cold test but germinator test and TTC test was higher percentage .