초록 ▼

Ⅳ. 연구개발결과
□ 상세기상 정보 생산 및 농업기상 정보 제공 체계 구축
▷ 지형 특성을 고려한 지표면 최적 상세 기상자료 생산 기법 도출
▷ 공간내삽 기법과 역학적 상세화 기법의 비교 및 최적 기법 도출
▷ 작물 주산지별 상세 기상정보 생산을 위한 응용프로그램 개발
- 상세 기상 도출 모듈 개발
- 프로그램 설계 및 알고리즘 개발
- 입출력 모듈 개발
▷ 관측 기상자료를 이용한 상세 기상정보 생산
▷ 작물별 생육시기별 및 주산지별 기상요소와 작물 생산량의 상관분석
▷ 주산지

Ⅳ. 연구개발결과
□ 상세기상 정보 생산 및 농업기상 정보 제공 체계 구축
▷ 지형 특성을 고려한 지표면 최적 상세 기상자료 생산 기법 도출
▷ 공간내삽 기법과 역학적 상세화 기법의 비교 및 최적 기법 도출
▷ 작물 주산지별 상세 기상정보 생산을 위한 응용프로그램 개발
- 상세 기상 도출 모듈 개발
- 프로그램 설계 및 알고리즘 개발
- 입출력 모듈 개발
▷ 관측 기상자료를 이용한 상세 기상정보 생산
▷ 작물별 생육시기별 및 주산지별 기상요소와 작물 생산량의 상관분석
▷ 주산지 기상요소별 경향성 분석

□ 관심 품목의 주산지별 생육, 생리장애 및 병해충 특성 DB 구축
▷ 무, 배추 주산지 시·군 및 대표 품종 선정
▷ 최근 20년간 여름 및 가을무, 배추 주산지역 생육, 병해충, 생리장애 자료 수집 및 D/B화
▷ 무, 배추 가을재배 작형 포장시험 및 생육특성 조사(2014-2015)
▷ 고추, 마늘, 양파의 주산지역 생육, 병해충, 생리장애 자료 수집 및 D/B화
- 주산지역 Soil Quality 분석
- 주산지 생육 상황 Monitoring
- 과거 병해충 발생 조사내용 분석
- 주요 병해충 발생 실태 조사
- 주산지별 주요 병해 발생 실태 조사
- 기상요소, 재배방법과 병 발생량 비교분석
- 주요 해충의 생태적 특성 분석

□ 관심 품목별 생장 모형 및 작황 예측 모형 개발
▷ 배추, 무, 마늘에 대하여 생육 프로세스 모형 개발
▷ 프로세스 모형을 이용한 결과와 작황조사 자료의 비교 분석
▷ 작물 생육 프로세스 모형을 이용한 작황 예측 가능성 검토
▷ 단수 결정 요인 세부 모형 개발
- 기온, 작물 잎 및 토양 표면 온도 변화 예측 모형 개발
▽ Scaling technique을 이용한 작물 엽온의 일중변화 양상 예측모델 개발
▷ 단수 제한 요인 세부 모형 개발
▽ 작물 Stress Index 모형 분석
- 주산지별 water balance 산정
▷ 포장 재배 시험 및 주산지 생육 monitoring에 근거한 Growth model 개발
- YLD = [Yo] + A/[(1+exp [-(X-B)/C])], 단, X는 생육 경과 일수 A, B, C는 계수 임.
▷ 기상자료를 이용한 작황 예측 다중회귀 모형 개발
▷ 생육 조사 및 기상자료를 이용한 작황 예측 비선형 확률 모형 개발

Abstract ▼

Crop yield must be dependent on the meteorological condition. This study suggested down scaling method and service system of meteorological information using observed and forecasted climate data for model study on main vegetable crops. first, we conducted comparison study with statistical and dynami

Crop yield must be dependent on the meteorological condition. This study suggested down scaling method and service system of meteorological information using observed and forecasted climate data for model study on main vegetable crops. first, we conducted comparison study with statistical and dynamic down scaling method, and found a more accurate values on dynamic downscaling method. The system of suggested on this study, built a database including ASOS climate data observed since 1961y and AWS climate data observed since 1996y. And the system real-time linkage with village scale forecasting from UM(Unified Model) data on KMA. In our result, include user manual of the system for the agricultural researcher and farmer. Actually, another part of our research group use our down scaled climate data from that system for the prediction of crop yield on main vegetable crops.
A panel analysis, multiple regression analysis and correlation analysis were carried out using mean temperature, maximum temperature, minimum temperature, precipitation and sunshine’s duration for estimation of vegetable crop’s yield. A trend analysis was carried out for knowing change of meteorological elements in the main producing areas of kimchi cabbage, radish, pepper, garlic and onion.
A crop growth model as well as yield prediction model is necessary to predict the yield of crops accurately in changing climate, but only limited information is available yet. This study was carried out to collect data on the yield, pest, disease and physiological disorder accumulated for last decades in the major producing area of kimchi cabbage, radish, red pepper, garlic and onion. The field trips carried out to investigate the yield, pest, disease and physiological disorder of kimchi cabbage and radish in the major producing area for two years (2014 – 2015) and to investigate growth and yield in different growth stages of kimchi cabbage and radish plants grown in experimental plots, in order to provide these information to other project teams developing crop growth models and yield prediction models.
Meteorological data such as temperature, relative humidity and photosynthetic active radiation were collected during the growing season. Yield components such as leaf weight, length and number for the crops and meteorological data were corrected during ten years in major vegetation. The DB system was established using these data, and it must be contribute to develop the estimation model of crop growth and yield.
A study was carried out to develop prototypes of napa cabbage, radish, garlic crop process models for estimating vegetable crop yield. The effect of temperature on development rate, leaf initiation and leaf area of napa cabbage, radish and garlic are also elucidated from the experiments of outdoor chamber and indoor chamber with different temperature treatments. The radiation use efficiency(RUE) was calculated from the relationships between intercepted radiation and dry weight of napa cabbage, radish and garlic crops. Three models were constructed using concept of RUE. These models are also established using Excel Add-in, called BuildIt, to aggregate and calculate of several physiological-process modules. We can find possibility to apply these models for estimating yields of vegetables by using daily meteorological data at cultivated areas. Several studies to make more accurate vegetable models are needed in area of making farm-level weather and soil data.
Anyway, water (or rainfall) and air temperature are very important factors on crop yield. Therefore in this study, a model to estimate the air, crop leaf and soil surface temperature was developed using scaling technique, and the agricultural water balance of main vegetable was investigated together with the other factor to affect on the crop growth.
In conclusion, to add to above process models, another three kinds of models to estimate crop yield of radish, kimchi cabbage, red pepper, garlic and onion were developed using meteorological data and survey data. A multiple regression model was developed based on the correlation between yield and meteorological factor using meteorological data. Another sigmoid model was developed base on the correlation between growth determination and yield using early growth survey data in the case of high correlation between growth data and yield. The other sigmoid model was developed base on the correlation between coefficient of the model and meteorological factor using meteorological data in the case of low correlation between growth data and yield.