Study on development of agricultural system model on a large scale

大规模农业系统模型开发研究

• 批准号: 15380178
• 负责人: TANI Hiroshi
• 金额: $ 10.3万
• 依托单位: HOKKAIDO UNIVERSITY
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Scientific Research (B)
• 财政年份: 2003
• 资助国家: 日本
• 起止时间: 2003 至 2004
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-15380178/
• 关键词: Agricultural production; Estimation; AMeDAS Data; Crop growth model; Satellite data; Simulation; grid data; 静止気象衛星データ; 日射量推定; 収量予測; 地理情報システム

Monitoring crop condition and production estimates on a large scale is important for food supply. The objectives of the study are (1)mapping meteorological values by use of Geostationary Meteorological Satellite data for solar radiation and by routine observation data for meteorological grid data on daily and hourly basis, (2)simulating crop growth by coupling of crop growth model and estimation model for meteorological data, (3)applying estimation technique for meteorological variables by satellite data to a foreign country, and (4)monitoring environmental information for agricultural production by dense observation points.The main results are as follows. (1)We have improved existing model for estimating solar radiation by use of clear sky recognition algorithm and land use data with RMS Error of 16%, and could estimate grid meteorological data (air temperature, wind speed and sunshine duration) from routinely observed data fairly well. (2)We have demonstrated the use of a crop simulation model together with solar radiation data estimated by GMS images and meteorological data. The system showed rice yield predictions at RMS Error of 65kg/10a. We expect that the distribution of crop growth and crop yield by the system developed in this study will be used for consulting farmers, analyzing crop damage by meteorological disaster and predicting crop yield change by climate change. (3)We have applied the method for estimating net radiation and precipitation by meteorological satellite data on Yellow River basin in China where water shortage is reducing agricultural production. The obtained results were satisfactory. (4)By using the data of a meteorological observation robot network, the wheat maturity period prediction map could be obtained by the grid of 250m, and the risk map of the frost damage of crops based on frost probability has been created.

大规模监测作物状况和产量估计对粮食供应很重要。研究的目的是：(1)利用地球静止气象卫星太阳辐射资料和气象网格日、小时常规观测资料绘制气象值；(2)将作物生长模型和气象资料估算模型耦合起来模拟作物生长；(3)将卫星资料气象变量估算技术应用于国外；(4)通过密集观测点监测农业生产环境信息。(1)利用晴空识别算法和土地利用数据对现有的太阳辐射估算模型进行了改进，均方根误差为16%，能够较好地从常规观测数据中估算网格气象数据(气温、风速和日照时数)。(2)结合GMS图像和气象资料估算的太阳辐射数据，演示了作物模拟模型的使用。该系统对水稻产量预测的均方根误差为65 kg/10a。我们期望通过本研究开发的系统所得到的作物生长和产量的分布将被用于咨询农民、分析气象灾害造成的农作物损失和预测气候变化对作物产量的影响。(3)将利用气象卫星资料估算黄河流域净辐射和降水的方法应用于缺水导致农业减产的中国流域。结果令人满意。(4)利用气象观测机器人网络数据，以250m网格为网格，得到小麦成熟期预报图，生成了基于霜冻概率的作物霜冻危害风险图。