• Proceedings of the Korea Water Resources Association Conference
• /
• 2017.05a
• /
• pp.199-199
• /
• 2017

Sustainable wheat production is of paramount importance for attaining/maintaining the food self-sufficiency status of the rapidly growing nation of Pakistan. However, the average wheat yield per unit area has been dwindling in recent years and the climate-induced variations in rainfall patterns and temperature regimes, during the wheat growth period, are believed to be the reason behind this decline. Crop growth simulation models are powerful tools capable of playing pivotal role in evaluating the climate change impacts on crop yield or productivity. This study was aimed to predict the plausible variations in the wheat yield for future climatic trends so that possible mitigation strategies could be explored. For this purpose, Aquacrop model v. 4.0 was employed to simulate the wheat yield under present and future climatology of the largest agricultural province of Punjab in Pakistan. The data related to crop phenology, management and yield were collected from the experimental plots to calibrate and validate the model. The future climate projections were statistically downscaled from five general circulation models (GCMs) and compared with the base line climate from 1980 to 2010. The model was fed with the projected climate to simulate the wheat yield based on the RCP (representative concentration pathways) 4.5 and 8.5. Under the worst, most likely future scenario of temperature rise and rainfall reduction, the crop yield decreased and water footprint, especially blue, increased, owing to the elevated irrigation demands due to accelerated evapotranspiration rates. The modeling results provided in this study are expected to provide a basic framework for devising policy responses to minimize the climate change impacts on wheat production in the area.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.21 no.3
• /
• pp.158-166
• /
• 2019

Implementing proper land management techniques, such as selecting the best crops and applying the best cultivation techniques at the farm level, is an effective way for farmers to adapt to climate change. Also it will be helpful if the farmer can get the information of agro-weather and the growth status of cultivating crops in real time and the simulated results of applying optional technologies. To test this, a system (web site) was developed to produce agro-weather data and crop growth information of farms by combining agricultural climate maps and crop growth modeling techniques to highland area for summer-season Chinese cabbage production. The system has been shown to be a viable tool for producing farm-level information and providing it directly to farmers. Further improvements will be required in the speed of information access, the microclimate models for some meteorological factors, and the crop growth models to test different options.

• Proceedings of the Korean Society of Crop Science Conference
• /
• 2022.10a
• /
• pp.235-235
• /
• 2022

Genome selection is a promising tool for plant and animal breeding, which uses genome-wide molecular marker data to capture large and small effect quantitative trait loci and predict the genetic value of selection candidates. Genomic selection has been shown previously to have higher prediction accuracies than conventional marker-assisted selection (MAS) for quantitative traits. In this study, the prediction accuracy of 10 agricultural traits in the wheat core group with 567 points was compared. We used a cross-validation approach to train and validate prediction accuracy to evaluate the effects of training population size and training model.As for the prediction accuracy according to the model, the prediction accuracy of 0.4 or more was evaluated except for the SVN model among the 6 models (GBLUP, LASSO, BayseA, RKHS, SVN, RF) used in most all traits. For traits such as days to heading and days to maturity, the prediction accuracy was very high, over 0.8. As for the prediction accuracy according to the training group, the prediction accuracy increased as the number of training groups increased in all traits. It was confirmed that the prediction accuracy was different in the training population according to the genetic composition regardless of the number. All training models were verified through 5-fold cross-validation. To verify the prediction ability of the training population of the wheat core collection, we compared the actual phenotype and genomic estimated breeding value using 35 breeding population. In fact, out of 10 individuals with the fastest days to heading, 5 individuals were selected through genomic selection, and 6 individuals were selected through genomic selection out of the 10 individuals with the slowest days to heading. Therefore, we confirmed the possibility of selecting individuals according to traits with only the genotype for a shorter period of time through genomic selection.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.23 no.4
• /
• pp.374-390
• /
• 2021

Growth and yield of field vegetable crops would be affected by climate conditions, which cause a relatively large fluctuation in crop production and consumer price over years. The yield prediction system for these crops would support decision-making on policies to manage supply and demands. The objectives of this study were to compile literatures related to onion and garlic and to perform data-mining analysis, which would shed lights on the development of crop models for these major field vegetable crops in Korea. The literatures on crop growth and yield were collected from the databases operated by Research Information Sharing Service, National Science & Technology Information Service and SCOPUS. The keywords were chosen to retrieve research outcomes related to crop growth and yield of onion and garlic. These literatures were analyzed using text mining approaches including word cloud and semantic networks. It was found that the number of publications was considerably less for the field vegetable crops compared with rice. Still, specific patterns between previous research outcomes were identified using the text mining methods. For example, climate change and remote sensing were major topics of interest for growth and yield of onion and garlic. The impact of temperature and irrigation on crop growth was also assessed in the previous studies. It was also found that yield of onion and garlic would be affected by both environment and crop management conditions including sowing time, variety, seed treatment method, irrigation interval, fertilization amount and fertilizer composition. For meteorological conditions, temperature, precipitation, solar radiation and humidity were found to be the major factors in the literatures. These indicate that crop models need to take into account both environmental and crop management practices for reliable prediction of crop yield.

• Korean journal of applied entomology
• /
• v.52 no.2
• /
• pp.133-140
• /
• 2013

The developmental times of the immature stages of Sogatella furcifera (Horvath) were investigated at ten constant temperatures (12.5, 15, 17.5, 20, 22.5, 25, 27.5, 30, 32.5, $35{\pm}1^{\circ}C$), 20~30% RH, and a photoperiod of 14:10 (L:D) h. Eggs were successfully developed on each tested temperature regimes except $12.5^{\circ}C$ and its developmental time was longest at $15^{\circ}C$ (22.5 days) and shortest at $32.5^{\circ}C$ (5.5 days). Nymphs successfully developed to the adult stage from $15^{\circ}C$ to $32.5^{\circ}C$ temperature regimes. Developmental time was longest at $15^{\circ}C$ (51.9 days) and it was decreased with increasing temperature up to $32.5^{\circ}C$ (9.0 days). The relationships between developmental rate and temperature were fitted by a linear model and seven nonlinear models (Analytis, Briere 1, 2, Lactin 2, Logan 6, Performance and modified Sharpe & DeMichele). The lower threshold temperature of egg and total nymphal stage was $10.2^{\circ}C$ and $12.3^{\circ}C$ respectively. The thermal constant required to complete egg and nymphal stage were 122.0 and 156.3 DD, respectively. The Briere 1 model was best fitted ($r^2$= 0.88~0.99) for all developmental stages, among seven nonlinear models. The distribution of completion of each development stage was well described by three non-linear models (2-parameter, 3-parameter Weibull and Logistic) ($r^2$= 0.91~0.96) except second and fifth instar.

• Asian-Australasian Journal of Animal Sciences
• /
• v.22 no.1
• /
• pp.57-71
• /
• 2009

Crop-livestock mixed farming systems depend on the efficiency with which nutrients are conserved and recycled. Home-grown forage is used as animal feed and animal excretions are applied to cultivated crop lands as manure. The objective of this study was to develop a mixed farming system model for dairy cattle in Japan. The model consisted of four sub-models: the nutrient requirement model, based on the Japanese Feeding Standards to determine requirements for energy, crude protein, dry matter intake, calcium, phosphorus and vitamin A; the optimum diet formulation model for determining the optimum diets that satisfy nutrient requirements at lowest cost, using linear programming; the herd dynamic model to calculate the numbers of cows in each reproductive cycle; and the whole farm optimization model to evaluate whole farm management from economic and environmental viewpoints and to optimize strategies for the target farm or system. To examine the model' validity, its predictions were compared against best practices for dairy farm management. Sensitivity analyses indicated that higher yielding cows lead to better economic results but higher emvironmental load in dairy cattle systems integrated with forage crop production.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.18 no.1
• /
• pp.99-106
• /
• 2023

Recently, many studies on crop yield prediction using deep learning technology have been conducted. These algorithms have difficulty constructing a linear map between input data sets and crop prediction results. Furthermore, implementation of these algorithms positively depends on the rate of acquired attributes. Deep reinforcement learning can overcome these limitations. This paper analyzes the performance of DQN, Double DQN and Dueling DQN to improve crop yield prediction. The DQN algorithm retains the overestimation problem. Whereas, Double DQN declines the over-estimations and leads to getting better results. The proposed models achieves these by reducing the falsehood and increasing the prediction exactness.

• Proceedings of the Korean Society of Crop Science Conference
• /
• 2017.06a
• /
• pp.146-146
• /
• 2017

The pod shattering or dehiscence is essential for the propagation of pod-bearing plant species in the wild, but it causes significant yield losses during harvest of domesticated crop plants. Identifying novel molecular makers, which are linked to seed-shattering genes, is needed to employ the molecular marker-assisted selection for efficiently developing shattering-resistant soybean varieties. In this study, a genetic linkage map was constructed using 115 recombinant inbred lines (RILs) developed from crosses between the pod shattering susceptible variety, Keunol, and resistant variety, Sinpaldal. A 180 K Axiom(R) SoyaSNPs data and pod shattering data from two environments in 2001 and 2015 were used to identify quantitative trait loci (QTL) for pod shattering. A major QTL was identified between two flanking single nucleotide polymorphism (SNP) markers, AX-90320801 and AX-90306327 on chromosome 16 with 1.3 cM interval, 857 kb of physical range. In sequence, genotype distribution analysis was conducted using extreme phenotype RILs. This could narrow down the QTL down to 153 kb on the physical map and was designated as qPDH1-KS with 6 annotated gene models. All exons within qPDH1-KS were sequenced and the 6 polymorphic SNPs affecting the amino acid sequence were identified. To develop universally available molecular markers, 38 Korean soybean cultivars were investigated by the association study using the 6 identified SNPs. Only two SNPswere strongly associated with the pod shattering. These two identified SNPs will help to identify the pod shattering responsible gene and to develop pod shattering-resistant soybean plants using marker-assisted selection.

• KOREAN JOURNAL OF CROP SCIENCE
• /
• v.29 no.2
• /
• pp.118-127
• /
• 1984

In order to find an appropriate model for rice crop-season, the possibility to utilize the geographical conditions instead of meteorological factors was examined on the data from the Local Adaptability Test(LAT) conducted over the country from 1962 to 1980. The mutiple regression model, $Y={\Upsilon}={\ss}{\sum}_{i=1}^n{\beta}^1X^iwas applied on seeding, transplanting, heading and marginal heading date, and multiple regression coefficients(\beta) and multiple correlation coefficients (R) were tested. Two varietal groups, japonica(1962-l971) and indica/japonica(l972-1980) were separately tested. The application of these established models, growth duration in nursery and paddy field, cultural season, and the relation between heading date and yield are reviewed.

• Proceedings of the Korean Society of Crop Science Conference
• /
• 2017.06a
• /
• pp.303-303
• /
• 2017

Japanese agriculture has faced to several threats: aging and decrease of farmer population, global competition, and the risk of climate change as well as harsh and variable weather. On the other hands, the number of large scale farms is increasing, because farm lands have been being aggregated to fewer numbers of farms. Cost cutting, development of efficient ways to manage complicatedly scattered farm lands, maintaining yield and quality under variable weather conditions, are required to adapt to changing environments. Information and communications technology (ICT) would contribute to solve such problems and to create innovative technologies. Thus we have been developing an early warning and decision support system to reduce weather and climate risks for rice, wheat and soybean production in Japan. The concept and prototype of the system will be shown. The system consists of a weather data system (Agro-Meteorological Grid Square Data System, AMGSDS), decision support contents where information is automatically created by crop models and delivers information to users via internet. AMGSDS combines JMA's Automated Meteorological Data Acquisition System (AMeDAS) data, numerical weather forecast data and normal values, for all of Japan with about 1km Grid Square throughout years. Our climate-smart system provides information on the prediction of crop phenology, created with weather forecast data and crop phenology models, as an important function. The system also makes recommendations for crop management, such as nitrogen-topdressing, suitable harvest time, water control, pesticide spray. We are also developing methods to perform risk analysis on weather-related damage to crop production. For example, we have developed an algorism to determine the best transplanting date in rice under a given environment, using the results of multi-year simulation, in order to answer the question "when is the best transplanting date to minimize yield loss, to avoid low temperature damage and to avoid high temperature damage?".