scholarly journals Yield Prediction Modeling for Sorghum–Sudangrass Hybrid Based on Climatic, Soil, and Cultivar Data in the Republic of Korea

Agriculture ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 137
Author(s):  
Jinglun Peng ◽  
Moonju Kim ◽  
Kyungil Sung
Keyword(s):  
Prediction Model ◽  
Korean Peninsula ◽  
Climatic Factors ◽  
Sunshine Duration ◽  
Model Construction ◽  
Yield Prediction ◽  
Linear Modeling ◽  
Southern Area ◽  
The Republic ◽  
Sorghum Sudangrass

The objective of this study was to construct a sorghum–sudangrass hybrid (SSH) yield prediction model based on climatic, soil, and cultivar information in the southern area of the Korean Peninsula. Besides, the effects of climatic factors on SSH yield were investigated simultaneously. The SSH dataset (n = 105), including Dry Matter Yield (DMY, kg/ha), Seeding-Harvest Accumulated Temperature (SHaAT, °C), Seeding–Harvest Accumulated Precipitation (SHAP, mm), Seeding–Harvest Sunshine Duration (SHSD, h), Soil Suitability Score (SSS), and cultivar maturity information, was developed for model construction. Subsequently, using general linear modeling method, the SSH yield prediction model was constructed as follows: DMY = 6.5SHaAT – 4.9SHAP + 13.8SHSD – 54.4SSS – 1036.4 + Maturity. The impacts of the accumulated thermal climatic variables and accumulated precipitation during crop growth on the variance of SSH yield in this region were confirmed. The summer-concentrated precipitation in the southern area of the Korean Peninsula exceeded the proper range of SSH water requirement and led to stresses to its yield production. Furthermore, to improve the data quality for high fitness model construction, the standard schedule for forage crop cultivation experiment in this region was recommended to be developed, especially under the data requirement in the context of the big data era.

scholarly journals Detecting Long-Term Dry Matter Yield Trend of Sorghum-Sudangrass Hybrid and Climatic Factors Using Time Series Analysis in the Republic of Korea

Agriculture ◽  
2018 ◽  
Vol 8 (12) ◽  
pp. 197 ◽  
Author(s):  
Befekadu Chemere ◽  
Jiyung Kim ◽  
Baehun Lee ◽  
Moonju Kim ◽  
Byongwan Kim ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Dry Matter ◽  
Climatic Factors ◽  
Climatic Conditions ◽  
Republic Of Korea ◽  
Dry Matter Yield ◽  
The Republic ◽  
Sorghum Sudangrass

Despite the gradual increase in livestock feed demands, the supply faces enormous challenges due to extreme climatic conditions. As the presence of these climatic condition has the potential to affect the yield of sorghum-sudangrass hybrid (SSH), understanding the yield variation in relation to the climatic conditions provides the ability to come up with proper mitigation strategies. This study was designed to detect the effect of climatic factors on the long-term dry matter yield (DMY) trend of SSH using time series analysis in the Republic of Korea. The collected data consisted of DMY, seeding-harvesting dates, the location where the cultivation took place, cultivars, and climatic factors related to cultivation of SSH. Based on the assumption of normality, the final data set (n = 420) was generated after outliers had been removed using Box-plot analysis. To evaluate the seasonality of DMY, an augmented Dickey Fuller (ADF) test and a correlogram of Autocorrelation Function (ACF) were used. Prior to detecting the effect of climatic factors on the DMY trend, the Autoregressive Integrated Moving Average (ARIMA) model was fitted to non-seasonal DMY series, and ARIMA (2, 1, 1) was found to be the optimal model to describe the long-term DMY trend of SSH. ARIMA with climatic factors (ARIMAX) detected significance (p < 0.05) of Seeding-Harvesting Precipitation Amount (SHPA) and Seeding-Harvesting Accumulated Temperature (SHAMT) on DMY trend. This does not mean that the average temperature and duration of exposure to sunshine do not affect the growth and development of SSH. The result underlines the impact of the precipitation model as a major factor for the seasonality of long-term DMY of SSH in the Republic of Korea.

Analysis of Crop Yield Prediction of Kharif & Rabi Jowar Crops Using Data Mining Techniques

2017 ◽  
Vol 7 (11) ◽  
pp. 79
Author(s):  
Sujata Mulik
Keyword(s):  
Data Mining ◽  
Crop Yield ◽  
Crop Production ◽  
Climatic Factors ◽  
Crop Productivity ◽  
Yield Prediction ◽  
Data Mining Techniques ◽  
Agriculture Sector ◽  
Using Data ◽  
Rabi Crops

Agriculture sector in India is facing rigorous problem to maximize crop productivity. More than 60 percent of the crop still depends on climatic factors like rainfall, temperature, humidity. This paper discusses the use of various Data Mining applications in agriculture sector. Data Mining is used to solve various problems in agriculture sector. It can be used it to solve yield prediction.  The problem of yield prediction is a major problem that remains to be solved based on available data. Data mining techniques are the better choices for this purpose. Different Data Mining techniques are used and evaluated in agriculture for estimating the future year's crop production. In this paper we have focused on predicting crop yield productivity of kharif & Rabi Crops. 

Prospects for development of the Russian energy projects on the Korean Peninsula

2020 ◽  
Vol 92 (3) ◽  
pp. 8-19
Author(s):  
O.V. Demina ◽  
Keyword(s):  
Transmission Lines ◽  
Korean Peninsula ◽  
Power Transmission ◽  
Russian Far East ◽  
Far East ◽  
Primary Energy ◽  
Energy Resources ◽  
Republic Of Korea ◽  
Energy Projects ◽  
The Republic

The article assesses prospects of the Russian-Korean cooperation and analyzes risks and opportunities of the trilateral energy projects on the Korean Peninsula. The author noted that energy sector is the key area of bilateral cooperation between Russia and the Republic of Korea, but it’s mainly represented by the trade in primary energy resources. The study identified the export potential of Russian hydrocarbons to the market of the Republic of Korea. As for the DPRK, the paper shows that within bilateral relations geopolitical interests prevail over the economic ones. The small capacity of the DPRK's domestic market and the absence of fixation sources do not allow considering it as an independent full-fledged market for the Russian energy resources. The main goal of the energy strategy of Russia and the Russian Far East is increasing the volume of exports of the primary energy resources to the APR countries. Russian prospects for the new product niches in the energy markets of the Republic of Korea are associated with the implementation of trilateral energy projects among Russia, the DPRK and the Republic of Korea. It includes creation of the interstate power transmission lines and construction of a gas pipeline. All parties are interested in these projects. As for Russia, it is primarily the expansion of energy exports, including occupation of the commodity niches in new markets, and strengthening of the political role in the region. As for the Republic of Korea, these projects mean diversification of supplies and costs’ reduction of the import energy resources. And as for the DPRK, these projects imply an additional source of financing (as payment for transit), improvement of the country's energy infrastructure and reduction of the deficit of energy resources. Despite the prospects, the author determined that in the near future implementation of the projects is unlikely due to the unresolved transit risks.

scholarly journals The Relationship between Tree-Ring Growths of Pinus densiflora and Climate from Three Mountains in Central Region, the Republic of Korea

Atmosphere ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 878
Author(s):  
Chang-Hyun Park ◽  
Ui-Cheon Lee ◽  
Soo-Chul Kim ◽  
Kwang-Hee Lee
Keyword(s):  
Tree Ring ◽  
National Parks ◽  
Central Region ◽  
Climatic Factors ◽  
Pinus Densiflora ◽  
Ring Width ◽  
Republic Of Korea ◽  
Logarithmic Curve ◽  
The Republic ◽  
The Relationship

To analyze the relationship between climatic factors (monthly mean temperature and total precipitation) and tree-ring growths of Pinus densiflora from the central region of the Republic of Korea, more than 20 trees were sampled from three national parks. The tree-ring chronology of Mt. Bukhan covering the period of 1917–2016 was assessed, as well as that of Mt. Seorak across 1687–2017 and Mt. Worak across 1777–2017. After cross-dating, each ring-width series was double-standardized by first fitting a logarithmic curve and then a 50 year cubic spline. Climate-growth relationships were computed with bootstrap correlation functions. The result of the analysis showed a positive response from the current March temperature and May precipitations for tree-ring growth of Pinus densiflora. It indicates that a higher temperature supply during early spring season and precipitation during cambium activity are important for radial growths of Pinus densiflora from the central region in the Republic of Korea.

Interval of days required for determining efficient relations between climatic factors and cotton flower and boll production

2002 ◽  
Vol 82 (3) ◽  
pp. 499-506 ◽  
Author(s):  
Zakaria M Sawan ◽  
Louis I Hanna ◽  
Willis L McCuistion
Keyword(s):  
Soil Temperature ◽  
Air Temperature ◽  
Surface Soil ◽  
Cultural Practices ◽  
Agricultural Research ◽  
Climatic Factors ◽  
Sunshine Duration ◽  
Field Trials ◽  
Cotton Production ◽  
Surface Soil Temperature

The cotton plant (Gossypium spp.) is sensitive to numerous environmental factors. This study was aimed at predicting effects of climatic factors grouped into convenient intervals (in days) on cotton flower and boll production compared with daily observations. Two uniformity field trials using the cotton (G. barbadense L.) cv. Giza 75 were conducted in 1992 and 1993 at the Agricultural Research Center, Giza, Egypt. Randomly chosen plants were used to record daily numbers of flowers and bolls during the reproductive stage (60 days). During this period, daily air temperature, temperature magnitude, evaporation, surface soil temperature, sunshine duration, humidity, and wind speed were recorded. Data, grouped into intervals of 2, 3, 4, 5, 6, and 10 d, were correlated with cotton production variables using regression analysis. Evaporation was found to be the most important climatic variable affecting flower and boll production, followed by humidity and sunshine duration. The least important variables were surface soil temperature at 0600 and minimum air temperature. The 5-d interval was found to provide the best correlation with yield parameters. Applying appropriate cultural practices that minimize the deleterious effects of evaporation and humidity could lead to an important improvement in cotton yield in Egypt. Key words: Cotton, flower production, boll production, boll retention

Relatii temporale între parametrii climatici si cresterile anuale ale speciilor forestiere (studiu de caz)

2019 ◽  
Author(s):  
Maria Nedealcov ◽  
◽  
Ala Donica ◽  
Ion Agapi ◽  
Nicolae Grigoras ◽  
...  
Keyword(s):  
Climatic Factors ◽  
Growth Parameters ◽  
Ring Width ◽  
Aridity Index ◽  
Quercus Petraea ◽  
Distribution Area ◽  
Natural Distribution ◽  
Annual Ring Width ◽  
Republic Of Moldova ◽  
The Republic

The forests on the natural distribution area from the silvosteppe zone, under the influence of climate change will experience major changes in their structure and functioning. The analysis of growth parameters for Fraxinus excelsior, Quercus petraea, Q. robur in three experimental areas from center of the Republic of Moldova indicates that the radial growth processes are influenced by the same complex of climatic factors, which differ being dendroclimatic response intensity. It has been shown that between the annual tree growth and forest aridity index - FAI, there are close correlations: the higher FAI values indicate the lower annual growth of the trees, and vice versa, low FAI values identify good development conditions of the stands (higher increases in the annual ring width).

Yield Prediction Model for Potato Using Landsat Time Series Images Driven Vegetation Indices

2018 ◽  
Vol 1 (1-2) ◽  
pp. 29-38 ◽  
Author(s):  
Imran Hossain Newton ◽  
A. F. M Tariqul Islam ◽  
A. K. M. Saiful Islam ◽  
G. M. Tarekul Islam ◽  
Anika Tahsin ◽  
...  
Keyword(s):  
Time Series ◽  
Prediction Model ◽  
Vegetation Indices ◽  
Yield Prediction ◽  
Time Series Images

scholarly journals Development of Heavy Rain Damage Prediction Model Using Machine Learning Based on Big Data

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Changhyun Choi ◽  
Jeonghwan Kim ◽  
Jongsung Kim ◽  
Donghyun Kim ◽  
Younghye Bae ◽  
...  
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Prediction Model ◽  
Prediction Models ◽  
Meteorological Data ◽  
Heavy Rain ◽  
Machine Learning Techniques ◽  
Damage Prediction ◽  
Explanatory Variables ◽  
The Republic

Prediction models of heavy rain damage using machine learning based on big data were developed for the Seoul Capital Area in the Republic of Korea. We used data on the occurrence of heavy rain damage from 1994 to 2015 as dependent variables and weather big data as explanatory variables. The model was developed by applying machine learning techniques such as decision trees, bagging, random forests, and boosting. As a result of evaluating the prediction performance of each model, the AUC value of the boosting model using meteorological data from the past 1 to 4 days was the highest at 95.87% and was selected as the final model. By using the prediction model developed in this study to predict the occurrence of heavy rain damage for each administrative region, we can greatly reduce the damage through proactive disaster management.

scholarly journals UAV-Based Hyperspectral and Ensemble Machine Learning for Predicting Yield in Winter Wheat

Agronomy ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 202
Author(s):  
Zhen Chen ◽  
Qian Cheng ◽  
Fuyi Duan ◽  
Xiuqiao Huang ◽  
Honggang Xu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Winter Wheat ◽  
Prediction Model ◽  
Grain Filling ◽  
Yield Prediction ◽  
Spectral Indices ◽  
Learner Model ◽  
Ensemble Machine Learning ◽  
Base Learner

Winter wheat is a widely-grown cereal crop worldwide. Using growth-stage information to estimate winter wheat yields in a timely manner is essential for accurate crop management and rapid decision-making in sustainable agriculture, and to increase productivity while reducing environmental impact. UAV remote sensing is widely used in precision agriculture due to its flexibility and increased spatial and spectral resolution. Hyperspectral data are used to model crop traits because of their ability to provide continuous rich spectral information and higher spectral fidelity. In this study, hyperspectral image data of the winter wheat crop canopy at the flowering and grain-filling stages was acquired by a low-altitude unmanned aerial vehicle (UAV), and machine learning was used to predict winter wheat yields. Specifically, a large number of spectral indices were extracted from the spectral data, and three feature selection methods, recursive feature elimination (RFE), Boruta feature selection, and the Pearson correlation coefficient (PCC), were used to filter high spectral indices in order to reduce the dimensionality of the data. Four major basic learner models, (1) support vector machine (SVM), (2) Gaussian process (GP), (3) linear ridge regression (LRR), and (4) random forest (RF), were also constructed, and an ensemble machine learning model was developed by combining the four base learner models. The results showed that the SVM yield prediction model, constructed on the basis of the preferred features, performed the best among the base learner models, with an R2 between 0.62 and 0.73. The accuracy of the proposed ensemble learner model was higher than that of each base learner model; moreover, the R2 (0.78) for the yield prediction model based on Boruta’s preferred characteristics was the highest at the grain-filling stage.

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