• Korean Journal of Agricultural and Forest Meteorology
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• v.16 no.4
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• pp.368-383
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• 2014

Climate change is expected to affect population density, phenology, distribution, morphological traits, reproduction and genetics of insects, and even in the extinction of insects. To develop novel research subjects for predicting climate change effect, basic information about biological and ecological data on insect species should be compiled and reviewed. For this reason, this study was conducted to collect the biological information on insect pests that are essential for predicting potential damage caused by insect pests in future environment. In addition, we compared domestic and foreign research trends regarding climate change effect and suggested future research subjects. Domestic researchers were rather narrow in the subject, and were mostly conducted based on short-term monitoring data to determine relationship between insects and environmental variables. On the other hand, foreign researches studied on various subjects to analyze the effect of climate change, such as changes in distribution of insect using long-term monitoring data or their prediction using population parameters and models, and monitoring of the change of the insect community structure. To determine change of the phenology, distribution, overwintering characteristics, and genetic structures of insects under climate change through development of monitoring technique, in conclusion, further researches are needed. Also, development of population models for major or potential pests is important for prediction of climate change effects.

• Korean Journal of Agricultural and Forest Meteorology
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• v.9 no.4
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• pp.228-233
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• 2007

Pear psylla, Caccopsylla pyricola (Homoptera: Psyllidae), is a serious insect pest in pear orchards. C. pyricola overwinters as adults under rough bark scales of pear trees. When the weather warms up in the spring, the overwintered adults become active, climb up to the tree branches, and inhabit on fruit twigs to lay eggs. This study was conducted to develop a forecasting model for the onset of upward-movement of overwintered C. pyricola adults to control them by timely spraying of petroleum oil. The adult population densities were observed under rough barks (B) and on fruit twigs (T) of pear trees. Relative upward-movement rates (R) were calculated as T/(B+T). Low threshold temperatures for the activation of overwintered C. pyricola adults were selected arbitrarily from 5 to $9^{\circ}C$ at a $1^{\circ}C$ interval. Then, the days (D) when daily maximum air temperatures were above each low threshold temperature were counted from 1 February until to the dates with R $\geq$ 0.8. The same methods were applied for the prediction of the first observation of eggs. The variation of coefficients (CV) for the mean Des were lowest with the low threshold temperature of $6^{\circ}C$. At this selected threshold temperature, the upward movement of C. pyricola adults occurred with 12 D and they started laying eggs with 25 D. In the field validation, the model outputs with the $6^{\circ}C$ threshold temperature reasonably well explained the observed data in Suwon and Cheonan in 2002. Practical usages of the model were also discussed.

• Korean journal of applied entomology
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• v.53 no.3
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• pp.311-315
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• 2014

Lantern fly(Lycorma delicatula) is a major invasive pest that causes withering symptom of agricultural crops by sucking tree sap and sooty mold symptom by producing honeydew. This study was conducted to investigate the occurrence pattern of lantern fly in grape orchards in Gyeonggi area and the effect of winter temperature on L. delicatula egg survival during 2010 to 2013. In Gyeonggi areas, overwintered L. delicatula eggs began to hatch from early May and nymphs peaked in mid May. Adults emerged from late July and laid eggs until early November. The survival of L. delicatula eggs during overwintering was largely affected by winter temperatures. The relationship between the number of days below a threshold temperature (x) in January and the survival rate of overwintering L. delicatula eggs (y) was using linear regression model. The best model selected by the lowest RSS (residual sum of square) between predicted and actual survival was y = -1.0486 x + 94.496 ($R^2=0.7067$) with $-11^{\circ}C$ of threshold temperature. These results should be helpful to conduct L. delicatula management programs, since the results provided relivable prediction for the winter survival of L. delicatula eggs and the phenology of egg hatch in the spring.

• Proceedings of the Korean Society of Crop Science Conference
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• 2023.04a
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• pp.7-7
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• 2023

Currently, the main technologies of various fourth industries are big data, the Internet of Things, artificial intelligence, blockchain, mixed reality (MR), and drones. In particular, "digital twin," which has recently become a global technological trend, is a concept of a virtual model that is expressed equally in physical objects and computers. By creating and simulating a Digital twin of software-virtualized assets instead of real physical assets, accurate information about the characteristics of real farming (current state, agricultural productivity, agricultural work scenarios, etc.) can be obtained. This study aims to streamline agricultural work through automatic water management, remote growth forecasting, drone control, and pest forecasting through the operation of an integrated control system by constructing digital twin data on the main production area of the nojinot industry and designing and building a smart farm complex. In addition, it aims to distribute digital environmental control agriculture in Korea that can reduce labor and improve crop productivity by minimizing environmental load through the use of appropriate amounts of fertilizers and pesticides through big data analysis. These open-field agricultural technologies can reduce labor through digital farming and cultivation management, optimize water use and prevent soil pollution in preparation for climate change, and quantitative growth management of open-field crops by securing digital data for the national cultivation environment. It is also a way to directly implement carbon-neutral RED++ activities by improving agricultural productivity. The analysis and prediction of growth status through the acquisition of the acquired high-precision and high-definition image-based crop growth data are very effective in digital farming work management. The Southern Crop Department of the National Institute of Food Science conducted research and development on various types of open-field agricultural smart farms such as underground point and underground drainage. In particular, from this year, commercialization is underway in earnest through the establishment of smart farm facilities and technology distribution for agricultural technology complexes across the country. In this study, we would like to describe the case of establishing the agricultural field that combines digital twin technology and open-field agricultural smart farm technology and future utilization plans.