초록

국외 주요 곡물 생산지대(미국 아이오와 등 5개주)를 대상으로 작황 추정 기술을 확대․고도화하고자 원격탐사 기반작물 수량 추정 회귀모형, 바이오매스 모형, 작물모형을 구축하고, 기상 상세 관측 및 예측 기술을 개발하였다. 본 연구를 통해 생산된 작황정보의 효율적 제공 체계를 구축하기 위해 선물거래 시점인 8월말을 기준으로 연구성과를 종합하여 작황보고서를 작성하고 위성영상, 기상자료 자동 수집 및 작황정보 표출시작품을 구축하였다.

(출처 : 요약서 3p)

Abstract ▼

< Project 1 : Study on crop yield estimation model enhancement in main crop area using RS>
Purpose&Contents
Maize, soybean crop yield estimation models using remote sensing were built for five states in Illinois, Iowa, Kansas, Missouri, and Nebraska to make early estimates as of August 30. The

< Project 1 : Study on crop yield estimation model enhancement in main crop area using RS>
Purpose&Contents
Maize, soybean crop yield estimation models using remote sensing were built for five states in Illinois, Iowa, Kansas, Missouri, and Nebraska to make early estimates as of August 30. The crop condtion report was drafted by synthesizing other cooperative projects and the future expansion plans were established by analyzing imported grain statistics and existing overseas crop reports.

Results
○ Based on the trends in grain quantitative fluctuations, except for increasing trends, a yield estimation models based on vegetation indices and meteorological data were established to estimate yields for the five states studied in 2018 and 2019, and except for a few specific states, results were generally within 10%.
○ In order to effectively use the crop information, at the end of August, when the futures market was purchased, a crop report centered on five US belts was prepared.
○ As a result of analyzing the necessary area of crop condition analysis of foreign crop producing countries, the United States, Brazil, Five countries, Argentina, Australia and Ukraine were selected and analyzed for their characteristics.

Expected Contribution
○ DB of research materials and pilot production and use of information provision system
○ Supporting the use of departments related to overseas grain observation by providing crop information on major overseas grain production areas by preparing summary reports

< Project 2 : Enhancement of observed meteorological data of major crop production areas>
Purpose&Contents
The purpose of this study is to advance weather observation data production algorithm over major crop production areas and to analyze changes in meteorological elements over the areas.
In order to achieve this objective, we developed a system and a technology for production of the accurate and promptive agro-meteorological information over the study areas and analyzed spatio-temporal variations of agro-meteorological elements (e.g., temperature, precipitation, solar radiation). In addition, we investigated the relationship between ago-meteorological variability over major crop production areas and yield variations.

Results
To improve accuracy of agro-meteorological information, we established the system for agro-meteorological observation data production over major crop production areas by applying statistical downscaling model to global reanalysis data. Five meteorological elements (i.e., mean temperature, maximum temperature, minimum temperature, precipitation, solar radiation) are produced over U.S. (50km resolution) and Corn Belt area (10km resolution). Moreover, spatio-temporal change patterns in meteorological elements are analyzed for 2 years (2018-2019).
Linkage analysis of changes in meteorological elements and crop condition over major crop production areas is also performed.

Expected Contribution
It will help to obtain an accurate crop information over inaccessible oversea grain production areas through securement of technology for production of agricultural environment data. It can contribute to establishment of stable grain supply and demand policy by producing and providing preemptive crop information on major oversea crop production area.

< Project 3 : Error correction in crop yield forecasting by using process-oriented crop growth model>
Purpose&Contents
Purpose: To minimize prediction error using crop model and weather data
Contents: Study on variation of error and uncertainty through crop model and meteorological data ensemble
Results
The model ensemble was found to help improve the accuracy of the predictions. In particular, errors could be reduced when the growth responses to the climate were different. The forecasting weather have to explain abnormal weather. For crops, the error due to weather was not huge in general weather, but the range of error increased with the combination of model and forecasting weather..

Expected Contribution
(Technical) Enhance accuracy by using ensemble method when predicting crop yield.
(economical) Preemptively respond to futures markets with improved prediction accuracy

< Project 4 : Integration of crop model and remote sensing product for crop yield prediction using a supercomputer>
Purpose&Contents
The goal of this study is to develop a high performance computing (HPC) system for prediction of crop yield in a region, which would support decision-making on policies of grain imports as well as food security. To meet the goal, an integrated system for crop yield predicting using both crop growth model and satellite image was developed to equip with HPC capability. Crop yield predicted using the prediction system was compared with the observed data in the mid-western US.
The crop growth index was also derived form satellite images in the region of interest improving algorithm for assessment of crop health status.

Results
The HPC based system for crop yield prediction was implemented using MPI-openMP hybrid approach, which allows to accelerate computation time over the midwestern US regions. nlopt library was applied to the prediction system for improvement in computation speed for the procedure to fit leaf area index (LAI) data derived from satellite images to the outputs of crop model. This system can be operated using the nurion system, which is the supercomputer available from the national supercomputer center. To automate the launch process, a script was written to submitting the job to queue of nurion system. It was found that spatial distribution of crop yield predicted and observed in the five states in the study region was relatively similar.

Expected Contribution
The system developed in the present study can be used to predict crop yield not only in the midwestern US but also in the major gain crop production regions including Brazil, Argentina, and Ukraine. The HPC system would require relatively short time period for crop yield prediction, which allows to design polices in time. This system can be applied to regions where food security is at risk, which can be used for food aid policy making. This system can also be used to assess the impact of climate change on crop production at a global scale.

< Project 5 : Production of downscaled regional climate information using statistical-dynamical chain over the major crop production areas>
Purpose&Contents
Developing the statistical-dynamical chain using dynamical downscaling and statistical downscaling, we produced the regional climate prediction over the major crop production areas.

Results
- Developed the statistical-dynamical chain for producing the regional climate prediction
ㆍAfter studying various statistical downscaling methods, we select one method providing better representations.
ㆍWe developed the statistical-dynamical chain by using selected statistical downscaling method.
- Produced the regional climate prediction over the major crop production areas by using statistical-dynamical chain for producing the regional climate prediction
ㆍ Produced regional climate prediction data (30km and 10km spatial resolution) over the major crop production areas by using regional climate model imposed initial and lateral boundary conditions obtained by global climate model.
ㆍ Produced regional climate prediction data (5km spatial resolution) over the major crop production areas by using statistical downscaling method imposed 10km WRF data.
- Based upon observed and predicted climate data, we produced the agricultural climate indices

Expected Contribution
- The 5km climate prediction data can be utilized as an input data of yield forecasting model predicting a growing and yields of the crop.
- The 5km climate prediction data can be used to establish national food supply plan.

< Project 6: Development of crop information service prototype in major grain areas>
Purpose&Contents
Development of crop information service prototype in major grain areas

Results
○ Database upgrade and deployment
- Analysis of prediction and image data from existing research
- Database deployment considered for performance and scalability to store products that will be generated through collaborative research
○ Development of crop information service prototype and generation of crop information report
- Design of crop information service and crop information report through overseas and domestic case studies
- Implementation and verification of crop information service prototype

Expected Contribution
○ Verification of the effectiveness of the system through the test operation
○ Providing proven information and services to crop information consumers

(출처 : Summary 9p)

목차 Contents

• 표지 ... 1
• 제 출 문 ... 2
• 보고서 요약서 ... 3
• 국 문 요 약 문 ... 4
• Summary ... 9
• 목차 ... 14
• 제 1 장 연구 개발 과제의 개요 ... 15
• 제1절 연구 개발 목적 ... 15
• 제2절 연구 개발의 필요성 ... 16
• 제3절 연구 개발 범위 ... 20
• 제 2 장 연구 수행 내용 및 결과 ... 22
• 제1절 위성 영상을 활용한 주요 곡물 생산지역 수량 추정 모형 고도화 ... 22
• 제2절 주요 곡물 생산지역의 기상관측자료 분석 고도화 ... 50
• 제3절 과정지향형 작물모형을 활용한 주요 곡물 생산지역 작황 추정 오차보정 기술 개발 ... 108
• 제4절 슈퍼컴퓨터 기반 작물 모형 및 위성 영상 통합 작황 예측 ... 120
• 제5절 통계-역학사슬을 이용한 주요 곡물생산지역에 대한 상세 기상예측정보 생산 연구 ... 178
• 제5절 주요 곡물 생산지역 작황정보 표출 시작품 구축 ... 200
• 제 3 장 목표달성도 및 관련분야 기여도 ... 213
• 제1절 목표대비 달성도 ... 213
• 제2절 정량적 성과(논문게재, 특허출원, 기타)를 기술 ... 216
• 제 4 장 연구 결과의 활용 계획 ... 218
• 제 5 장 연구 개발 결과의 보안 등급 ... 220
• 제 6 장 연구시설·장비종합정보시스템에 등록한 연구시설·장비 현황 ... 221
• 제 7 장 연구개발과제의 대표적 연구실적 ... 222
• 제 8 장 기타사항 ... 224
• 제 9 장 참고문헌 ... 225
• 끝페이지 ... 232