Crop monitoring and yield forecasting MARS activities in Asia

Transcription

1 Crop monitoring and yield forecasting MARS activities in Asia Rémi Lecerf European Commission, Joint Research Centre GLOBCAST dissemination event Conference Centre Albert Borschette Brussels, 30 September 2015

2 Overview Focusing on the 2 main Asian producers China (92 M ha) India (74 M ha) 1. Factsheet of Chinese and Indian production 2. Yield forecast for Current limitations/advantages of the system

3 National production and balance China 1 st producer of rice and wheat 2 nd producer of maize India 2 nd producer of rice 2 nd producer of wheat Source: FAOSTAT China: Imports increasing slowly India: Increasing exports Source: USDA Source: USDA

4 Evolution of the production Increasing production since 1990 China Main variations are due to yields increase while the area is constant Area of maize increasing in China as a consequence of a strong demand Variations of yields are linked to climate India Source: FAOSTAT

5 India Spatial distribution of crops Rice production located on eastern coast and Ganges river valley Wheat production located on the northwestern states Rice Wheat

6 China Distribution of production Winter wheat cultivated in north china plain Maize cultivated in all provinces, in winter, spring, summer and autumn Middle rice cultivated on the reaches of the Yangtze Winter wheat Middle rice Grain maize Early rice Late rice

7 MARS Asia Bulletin Published in April May June July August September October November Update Report May/2015 Wheat and Rabi rice in India Update Report August/2015 Wheat, rice and maize in China Update Report May/2015 Wheat in China Full bulletin to be delivered Oct/2015 China India

8 Yield forecasting strategy Statistics available at GAUL-1 Late availability of statistical data (One year delay) Analyst forecasts covering more than 95% of the production: Scenario analysis Regression analysis Automatic forecast for all remaining regions Regression or scenario analysis Trend or average

9 China Yield forecast At national level, crop yield is increasing compared to the average Grain maize and winter wheat production at record Crop Area x 1000 ha MARS 2015 forecasts Yield t/ha Production x 1000 t. Avg 5yrs Diff % 2015/2014 Diff % 2015/5yrs Avg 5yrs Diff % 2015/2014 Diff % 2015/5yrs Winter wheat Spring wheat Total wheat Early rice Middle rice Late rice Total rice Grain maize

10 China Yield forecast Below average rainfall in the north of China Heavy rainfall during harvest of early rice Area x 1000 ha 2015 MARS 2015 forecasts Yield t/ha Avg 5yrs Diff % 2015/5yrs Production x 1000 t. Diff % 2015 Avg 5yrs 2015/5yrs China 37, , , Henan 3, , , Shandong 3, , , Hebei 3, , , Anhui , , Xinjiang , , Hubei , , Shanxi 1, , , Yunnan 1, , , Guizhou , , Chongqing , , Guangxi , , Inner Mongolia 3, , , Liaoning 2, , , Jilin 3, , , Heilongjiang 5, , , Others 1, , ,

11 India Yield forecast Negative outlook for wheat due to lower yields Yields of kharif rice also forecast down Positive outlook for Rice Rabi Crop Area x 1000 ha Yield t/ha Production x 1000 t Avg 5yrs %15/14 %15/5yrs 2014 MARS 2015 forecasts Avg 5yrs %15/14 %15/5yrs Avg 5yrs %15/14 %15/5yrs Rabi Wheat 30,163 30,396 29, ,226 89,144 89, Rabi Rice 4,425 4,412 4, ,590 15,079 13, Kharif Rice 39,371 39,445 38, ,312 89,499 86,

12 India Yield forecast Rabi Wheat Intense rainfall in March and April coincided with harvesting of wheat in northwestern states (most important producing areas for wheat) Country 2014 MARS 2015 forecasts Yield t/ha Avg 5yrs %15/14 %15/5yrs India Uttar Pradesh Punjab Haryana Madhya Pradesh Rajasthan Bihar Gujarat Maharashtra Others

13 India Yield forecast Kharif Rice The monsoon brought less rainfall than expected. Conditions are drier than usual since mid-august in the northern and southern states Country 2014 MARS 2015 forecasts Yield t/ha Avg 5yrs %15/14 %15/5yrs India Uttar Pradesh Punjab West Bengal Andhra Pradesh Orissa Chhattisgarh Bihar Tamil Nadu Assam Haryana Karnataka Maharashtra Jharkhand Madhya Pradesh Gujarat Others

14 CGMS Asia Current limitations Remote sensing data are complementary to crop model indicators in the north of China and India South of China, more investigations are needed on remote sensing data (cloudiness, intense crop rotations) Despite the current crop model improves the forecasts, better results could be obtained by improving meteorological data ECMWF (European Center for Medium Range Forecasts) data are the output of a meteorological model

15 Remote sensing data Limitations are found in southern half of China Cloudy data Double or triple cropping systems with several crop rotations NDVI: normalized Difference Vegetation Index Remote sensing indicator related to photosynthetic activity and biomass

16 ECMWF data reliability Comparison of rainfall: ECMWF data/wmo stations Data are more reliable on arable land Increasing errors in tropical climate Reliability depends on years, season and regions Deviations increases in case of ENSO (El Nino Southern Oscillation)

17 ECMWF data reliability Comparison of temperatures: ECMWF data/wmo stations Reliability decreases with relief Underestimated temperatures are delaying the simulations of crops

18 ECMWF data reliability Rainfall anomalies in India (summer 2015) Difference between rainfall from 1 June to 16 September 2015 and long term average

19 Reliability of the crop model (China) Mean errors of automatic forecasts using a regression analysis with a linear trend from 2003 to 2013 Crop model variables improves yield forecast against a simple trend Noticeable improvement for middle rice and grain maize Mean errors

20 Reliability of the crop model (China) Errors are increasing depending on meteorological data and crop calibration Improving meteorological data will improve crop model calibration and forecast reliability Grain Maize Middle Rice

21 Conclusion Production of wheat is forecast below the average in India Concerns about ENSO impact on rice production in India Positive outlook for Chinese production particularly grain maize Further improvements of the system are foreseen firstly on meteorological data Secondly on crop model calibration