Relationship Between Some Characteristics of the Corn Kernel Pericarp and Resistance to the Rice Weevil (Coleoptera: Curculionidae)1

1983 ◽  
Vol 76 (4) ◽  
pp. 797-800 ◽  
Author(s):  
L. A. Gomez ◽  
J. G. Rodriguez ◽  
C. G. Ponelelt ◽  
D. F. Blake
Keyword(s):  
Rice Weevil ◽  
Corn Kernel

PENGARUH POPULASI TANAMAN DAN PENGGUNAAN MULSA TERHADAP POPULASI GULMA DAN HASIL PUCIL JAGUNG MANIS (ZEA MAYS SACCHARATA STURT) DI LAHAN KERING

AGRICA ◽  
2020 ◽  
Vol 6 (2) ◽  
pp. 124-133
Author(s):  
I Ketut Arsa Wijaya
Keyword(s):  
Zea Mays ◽  
Block Design ◽  
Plant Population ◽  
Cyperus Rotundus ◽  
Corn Kernel ◽  
Plastic Mulch ◽  
Rice Plants ◽  
Straw Mulch ◽  
Combination Treatments ◽  
Randomized Block Design

This study was conducted in Balai Benih Induk (BBI) Luwus, Baturiti District, Tabanan Regency, with the high of place 506m above sea level occurring in March to May 2014. This research used randomized block design arranged per factor, namely population of plants (P) including three steps: P1=80.000 plants per hectare, P2= 100.00 plants per hectare, P3= 120.000 per hectare; and mulch (M) that include: M0= without mulch and without cleaning, M1= without mulch with clean weeding, M2= rice plants straw mulch (5 ton per hectare) and M3= black silver plastic mulch. Eventually, there were 12 combination treatments, each of which was repeated 3 times to obtain 36 land slots. The research indicates that there was no real interaction between the treatment of plant population and mulch usage in all variables observed. The 100.000 plant population per hectare can produce the weight of dry and fresh oven corn kernel without highest cornhusk that was to say 4.79 tons and 0.53 tons or 11.66 % and 20.45% higher compared to 120.000 plant population per hectare. The use of black silver plastic and rice plants straw mulch can produce the weight of fresh and dry oven corn kernel without cornhusk per hectare each of which is 4.69 tons, 4.57 tons, and 0.54 tons, 0.47 tons or 26.76%, 38.46 %, and 23.51 %, 20.51 % higher than without mulch and without clean weeding. Clean weeding treatment can produce the weight of fresh and dry oven corn kernel without cornhusk per hectare with its weight of 4.63 tons and 0.51 tons or 25.35 % and 35.90 % higher than without mulch and cleaning. Clean weeding treatment was not obviously different from mulch treatment. The identification of weed types were 19 kinds of weeds and Cyperus rotundus L. Weeds. They have the highest density and each absolute frequent of 1249.333 stems m-2 and 1.00.

The Effects of Selection Upon Protein Quality in the Corn Kernel 1

1949 ◽  
Vol 41 (9) ◽  
pp. 399-403 ◽  
Author(s):  
Kenneth J. Frey ◽  
B. Brimhall ◽  
G. F. Sprague
Keyword(s):  
Protein Quality ◽  
Corn Kernel

A Technique for Selecting for Rice Weevil Resistance in Corn in the Laboratory 1

Crop Science ◽  
1964 ◽  
Vol 4 (1) ◽  
pp. 109-110 ◽  
Author(s):  
F. S. McCain ◽  
W. G. Eden ◽  
D. N. Singh
Keyword(s):  
Rice Weevil

scholarly journals Performance of Selected Grain Protectants Against Maize/Rice Weevil in Stored Field Corn, 2020

2021 ◽  
Vol 46 (1) ◽  
Author(s):  
Don Cook ◽  
Meg Threet ◽  
Jeff Gore ◽  
Whitney Crow ◽  
Angus Catchot
Keyword(s):  
Rice Weevil ◽  
Field Corn ◽  
Grain Protectants

Rice cultivars affect fitness-related characteristics and digestive physiology of the rice weevil, Sitophilus oryzae (L.) (coleoptera: Curculionidae)

2021 ◽  
Vol 93 ◽  
pp. 101821
Author(s):  
Mahdi Jalaeian ◽  
Mozhgan Mohammadzadeh ◽  
Mohammad Mohammadzadeh ◽  
Ehsan Borzoui
Keyword(s):  
Sitophilus Oryzae ◽  
Rice Weevil ◽  
Rice Cultivars ◽  
Digestive Physiology

scholarly journals DeepCorn: A semi-supervised deep learning method for high-throughput image-based corn kernel counting and yield estimation

2021 ◽  
Vol 218 ◽  
pp. 106874
Author(s):  
Saeed Khaki ◽  
Hieu Pham ◽  
Ye Han ◽  
Andy Kuhl ◽  
Wade Kent ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Throughput ◽  
Learning Method ◽  
Corn Kernel ◽  
Yield Estimation

Modelling of corn kernel pre-treatment, drying and processing for ethanol production using artificial neural networks

2021 ◽  
Vol 162 ◽  
pp. 113293
Author(s):  
Neven Voca ◽  
Lato Pezo ◽  
Anamarija Peter ◽  
Danijela Suput ◽  
Biljana Loncar ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Ethanol Production ◽  
Corn Kernel ◽  
Artificial Neural ◽  
Pre Treatment
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