[ieee 2012 first international conference on agro-geoinformatics - shanghai, china...

Yield estimation of winter wheat in North China Plain by using crop growth monitoring system (CGMS)

TENG Fei Key Laboratory of Agri-informatics, Ministry of Agriculture

Institute of Agricultural Resources and Regional Planning of Chinese Academy of Agricultural Sciences

Beijing, China [email protected]

CHEN Zhongxin Key Laboratory of Agri-informatics, Ministry of Agriculture



WU Wenbin Key Laboratory of Agri-informatics, Ministry of Agriculture



Huang Qing Key Laboratory of Agri-informatics, Ministry of Agriculture



Li Dandan Key Laboratory of Agri-informatics, Ministry of Agriculture



Xia Tian Key Laboratory of Agri-informatics, Ministry of Agriculture



Abstract—Crop Growth Monitoring System (CGMS) was originally developed by European Union based on a crop growth model (WOFOST) with the goal of regional yield estimations for major crops. The CGMS, driven by the geographic information system and the crop growth model, is composed of three main components: weather monitoring, crop monitoring and crop yield forecasting. In this study, the CGMS is introduced into China Agriculture Remote Sensing Monitoring System (CHARMS) to improve its ability of crop yield estimation. To do that, Hebei Province in North China Plain is selected as the study area. The CGMS is localized and the important parameters are verified by using local ground truth data. Its input datasets is divided into two parts, i.e., static and dynamic data. The former includes soil, crop distribution, phenology, administrative maps, the later includes the weather data, time-series remotely sensed data, and crop statistics. After that, the CGMS is used to estimate the crop yield for winter wheat in Hebei province. The historical crop yield data is used to validate the model estimations. The results show that the localized CGMS can monitor well the crop growth processes and is able to estimate the crop yield at regional scale. The CGMS can serve the agricultural macro-economic regulation and control, the management of agricultural production, as well as the food warning system of China.

Keywords- CGMS; Hebei Province; winter wheat; yield prediction

I. INTRODUCTION Crop yield information is of great importance for food

security early warning, cropping activity management and agricultural commodity trade in a country or a region. Lots of methods, such as statistical model and agro-climate model, were used for crop yield estimation. Since 1980s, crop growth model gains more attention from scientific community due to its mechanisms and accuracy. [10, 15, 16]Yet, most crop models are originally a site-specific model, and use a daily time increment to simulate weather, hydrology, soil erosion by wind and water, nutrient cycling, tillage, crop management and growth, and field scale costs and returns. It is thus not possible to use the original model directly for large-area applications. However, by integrating model with GIS, the crop model gains the possibility of estimating crop yields from field level to small country or sub-regional scale.

Crop Growth Monitoring System (CGMS) developed by European Union by combing the crop growth model (WOFOST) and GIS, has the capability of estimating regional yield for major crops. In this study, the CGMS was introduced into China Agriculture Remote Sensing Monitoring System (CHARMS) to improve its ability of crop yield estimation. The CGMS was localized and the important parameters were

This project was supported by the International S&T Cooperation Projects of China (2010DFB10030); by the National Natural Science Foundation of China ( 41001246).

verified by using local ground truth data. After that, the CGMS was used to estimate the crop yield for winter wheat in Hebei province.

II. STUDY AREA North China Plain (NCP), the second largest plain in China

and located in northern latitude 32°-40° and eastern longitude 114° -121°, was selected as the study area. The NCP starts from Taihang Mountain Range and western Henan mountain land in the west and extends to the Yellow Sea, the Bohai Sea and Shandong Hills in the east. It crosses from the Yanshan Mountain Range to the TongBaiShan and the Dabie Mountains in the southwest part, and northern Jiangsu, Anhui in southeast. The NCP is also connected to the middle and lower Yangtze plain. Hence, it extends in Beijing, Tianjin, Hebei Province, Shandong Province, Henan Province, Anhui Province and Jiangsu Province etc. The NCP has an area of 300 thousand square kilometers, and it flats terrain. Most area is 50 meters below sea level. As a result, it is a typical alluvial plain. Furthermore, the NCP has a warm temperate zone monsoon climate, so the four seasons change significantly. The Huaihe river basin in the south subtropical transition to the region, and the temperature and precipitation are higher than north. Generally, the annual average temperature is 8 ~ 15 ℃ and winter there is cold and dry in NCP. The NCP farmland area accounts for about 40% of the cultivated area. Therefore, it is our largest and the most important wheat-producing area.

III. METHODS The CGMS, driven by the geographic information system

and the crop growth model (WOFOST 7.1), is composed of three main components: weather monitoring, crop monitoring and crop yield forecasting. WOFOST is a mechanistic model that explains crop growth on the basis of the underlying processes, such as photosynthesis, respiration and how these processes are influenced by environmental conditions. Through a lots of localization and verification processing, CGMS-China system was established and used for crop growth condition monitoring and yield forecasting in China. ORACLE tool was used for input data storage and management, crops parameters calibration, CGMS running and results visualization.

A. Level 1: Weather Monitoring The weather monitoring component is the first level of

CGMS. It consists of the two following activities: acquisition, checks and processing of daily meteorological station data, and spatial interpolation to a regular climatic grid. The daily meteorological station data was spatially interpolated into 50 km*50km climatic gridded data for 1999 to 2009. Major climatic variables include air temperature, precipitation, wind speed, vapour pressure, radiation global and transpiration data. The model inputs meteorological data for time series analysis and arrive at the information of large area. Output results such as average daily temperature, climatic water balance, global radiation, longest heat wave period, potential evapotranspiration, precipitation, temperature sum, etc. (e.g. Fig.1).

Figure 1. Average daily temperature from Dec.1st to Dec.7st 2009

B. Level 2: Crop Monitoring The crop monitoring component produces simulated crop

indicators like biomass and yields to show the effect of recent weather on crop growth. [18]It is based on the meteorological statistics of ECMWF (European Centre for Medium-Range Weather Forecasts), soil data from FAO (Food and Agriculture Organization), phenology data by the Ministry of Agriculture, crop background data from remote sensing applications etc. It also implements the WOFOST model calibration.

Figure 2. Example of a figure caption. (figure caption)

C. Level 3: Crop Yield Forecasting Comparative analysis and forecasting simulation of crops

output by using statistical data of North China winter wheat from National Bureau of Statistics at the county level and production.

Figure 3. The second Dekad of July to north China winter wheat yield modeling

IV. RESULTS AND ANALYSIS Some winter wheat parameters used by WOFOST crop

model in CGMS-China were calibrated by using remote sensing data (Such as LAI, NDVI, etc.)[2-5]. Part of the crop parameters calibrated was listed in Table I. After a lot of operation, simulated data at Level 1 and Level 2 were input into the Level 3 to forecast the winter wheat yield for 1998-2009.

TABLE I. CROP MODEL PARAMETER TABLE

CGMS Main Parameters Of Winter Wheat Parameter Description Unit Value

AMAXTB

Maximum leaf CO2 assimilation rate as a function of development stage of the crop.

kg/ha/hr 39.1

DLC Critical day length (photoperiod). hr 8.0

DLO Optimum day length (photoperiod). hr 12.2

LAIEM Leaf area index at emergence. ha/ha 0.13

SLATB Specific leaf area as a function of development stage.

ha/kg 0.0018

TSUM1 Thermal time from emergence to anthesis. ℃/d 1009.2

TSUM2 Thermal time from anthesis to maturity. ℃/d 603.3

The ground truth data taken from Hengshui and Langfang counties was used to validate the model simulation results. The comparison shows that the CGMS estimated crop yield is consistent with the ground surveyed crop yield as shown in Figure 4. Overall, the CGMS has more than 88% accuracy of yield forecasting for winter wheat in NCP. Table II shows the comparison between simulate yields and statistic yields in NCP. It can be seen that these two show a very high consistency[1, 6, 8, 9, 18].

Figure 4. Relation of crops yield simulation value and the actual value.

TABLE II. CROP MODEL PARAMETER TABLE

Comparison of statistic and simulated yields from 1998-2009

Year Statistic Yield/(kg·hm-2)

Simulated Yield/(kg·hm-2)

Accuracy/%

1998 4992.244 4690.216 94.0%

1999 2928.345 3306.577 88.6%

2000 3909.669 3711.188 94.9%

2001 4155.389 4312.062 96.4%

2002 4331.285 4319.576 99.7%

2003 5059.210 5212.725 97.1%

2004 5450.172 5349.800 98.2%

2005 5434.836 5772.332 94.2%

2006 5761.902 5252.573 91.2%

2007 5597.200 5414.858 96.7%

2008 5932.523 6210.867 95.5%

2009 5644.232 5859.620 96.3%

V. CONCLUSION This study shows that CGMS has a good applicability in

large-scale crop yields forecasting. [11-13]There are some uncertainties in this study. First, although there are some quality controls in each level, there are still some errors existing in each level, which may influence the final results. Second, this study used a very big amount of input data, the different data sources and processing methods may have some direct impacts on the results. Moreover, a lot of improvement should be further made to make the CGMS more reliable and accurate in large-scale crop yield estimations so as to better provides support to the macro control of the food production of the whole nation.

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