기상요인을 고려한 단수예측모형 개발 연구

Abstract

현행 농업관측사업은 주요 농축산물의 재배, 작황, 생산, 출하, 재고, 소비 등향 및 해외시장 정보 등을 분석하여 품목별 수급 및 가격 예측 정보를 제공하고 있다. 이러한 관측 정보는 주로 출하기 1~2개월 전의 단기관측을 통해 수급안정에 기여해 왔으나, 기상요인의 급격한 변화에 대한 대응이 미흡하였다. 따라서 최근 기후변화와 관련하여 기상요인이 중요한 변수로 대두됨에 기상요인을 고려한 수급예측의 필요성이 대두되었다.

기상요인을 고려한 단수모형예측이란 다양한 기상변수를 이용하여 생산량을 예측하고 그 수준에 따라 출하기 가격을 예측하여 적극적으로 공급 조절을 도모하기 위한 것이다. 이 연구에서는 이러한 기상요인을 바탕으로 쌀, 콩, 고랭지 배추 3개 품목에 대한 단수예측모형을 개발하였다.

본 연구는 기존 연구의 점추정, 기상요인과의 선형성, 최소자승법의 문제점을 극복하기 위해 확률추정, 이차형식, 패널분석으로 접근하여 추정방법과 예측방법의 개선을 시도하였다는 점에서 의미가 있다.

The main purpose of this study is to build a crop yield forecasting model for rice, soybean, and the summer Chinese cabbage cultivated in highlands with meteorological elements taken into account. The difference compared with the previous studies and the main outcome of this study are as follows: first, this study used a stochastic method to overcome the drawback of point estimation by using meteorological scenarios; second, model specification was changed from a linear function to a quadratic function form (such as the concave function to the zero point) to find optimal points on each element; third, a panel data analysis was used to enhance the degree of freedom. The panel analysis was used with a two-way fixed effect model considering cross-section and time period to find unbiased and consistent estimates of meteorological elements. Based on the analysis results, this study developed a stochastical crop yield forecasting model with a variety of meteorological scenarios. In addition, the possibility of introducing an EPIC(Erosion Productivity Impact Calculator) model is reviewed in order to overcome the limitations of our model. An estimation of rice yield was made using a regional panel data of over the recent 10 years and with mean temperature, effective cumulative temperature, sunshine hours, and daily temperature range as independent variables. The results using a fixed effects model confirm that the average temperature is a quadratic form and that the rice yield is highly affected by effective cumulative temperature before the grain filling stage and by the mean temperature during the grain filling stage. We set up a total of 1,296 scenarios at the end of September in 2011 to forecast rice yield based on weather data provided by Korea Meteorological Administration. Reducing the scenarios and replicating the estimation as the forecast time goes close to the target time, which was the end of September in 2011, we finally obtained the result that the target rice yield would be 496kg based on our scenario in the middle of September in 2011. Our forecast is not different from the real rice yield of the target time, 497kg, announced by the National Statistical Office through an actual inspection. An estimation of yield for the summer Chinese cabbage was made using a main producing district's panel data of over the recent 10 years and with high temperature and rainfall as independent variables. The results using a fixed effects model confirm that both high temperature and rainfall take a quadratic form. The result of the yield forecasting model of summer Chinese cabbage shows that the yield is higher than 2010's but less than average year. A soybean yield estimation was made using a regional panel data from 1980 to 2010, and except for Jeju, and with average temperature, sunshine duration, and rainfall as independent variables. The results using a fixed effects model confirm that both average temperature and rainfall take a quadratic form. This model used provincial weather data because we could not access the yield data of cities. So we can not determine the relationship between weather component and soybean yield. The yield forecasting model of rice, soybean, and summer Chinese cabbage cultivated in highlands has an implication that we can know their yield before the National Statistical Office announces its researched yield results. Therefore, we can use yield forecasts as basis data to prepare measures for any imbalance in supply and demand. The limitation of this study is that it did not consider the damage caused by diseases and insects and climate change because we don't have experiment data on yields of rice, soybean, and the summer Chinese cabbage cultivated in highlands. Therefore, a study for obtaining the experimental data has to be conducted by the Rural Development Administration beforehand. In the future study we need to merge EPIC models and the statistic yield forecasting model of this study to enhance the accuracy of crop yield forecasting.

Researchers: Sukho Han, Byounghoon Lee, Misung Park, Junho Seung, Hyunseok Yang, Sungchul Shin Research period: 2011. 7. - 2011. 12. E-mail address: shohan@krei.re.kr