Climate Induced Changes in Maize Potential Productivity in Heilongjiang Province of China

Wang Xiufen, Institute of Agriculture Resources and Regional Planning,wangxf@mail.caas.net.cnYangYanzhao, Institute of Geographical Sciences and Natural Resources ResearchYou Fei, Institute of Agriculture Resources and Regional Planning, yofae@sina.comLi Wenjuan, Institute of Agriculture Resources and Regional Planning

Outline

Background1

Data and models2

Results3

Discussion and Conclusion4

1 BackgroundGlobal climate change is unequivocal.

Many natural systems are being affected by regional climate changes, including crop production system.

The likely impacts of climate change on crop production have been studied widely either by experimental data or by crop growth simulation models.

However, studies of potential crop production capabilities affected by climate change in long time series remains relatively rare.

2 Data and modelsStudy area

Data sources

Meteorological data were obtained from the National Climatic Centre of the China Meteorological Administration

The land use map and administrative boundary maps of Heilongjiang province were collected from Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences

Models

Climate change analysis : the least squares linear model

xi＝a+bti i=1,2,…,n

xi is one of the climate variables(temperature or precipitation)ti is the time corresponding to xia is constantb is the regression coefficienta and b are estimated by the least squaresThe positive and negative sign of b represent the change trend of the climate variable , when b＞0, the climate variable increase with the time rise, vice versa. b×10 are the climate tendency rates, units are ℃ per decade or mm per decade.

•Climate change scenarios

mean daily temperature increase(℃) mean daily rainfall decrease(%)

Baseline(1980-2009) — —

Scenarios1 0.5 5Scenarios2 1.0 10Scenarios3 1.5 15

Three climate change scenarios used in this study

Potential Productivity Model (Agro-Ecological zones Model)

•On the basis of the calculation of LTPP, the obtained relative yield decrease factor f(p) is then applied to the calculation of CPP. ● The formula for calculating the CPP is as follows:

YC= YT · f(p)YC = the climatic potential productivity (CPP) of maize[kg/ha],YT = the light-temperature potential productivity (LTPP) of maize [kg/ha],f(p)= precipitation effective coefficient, f(p) is defined as follows:

Ky = yield response factor, P =effective precipitation, ETm = Kc ×ET0, Kc=crop coefficient, ET0=Reference Evapotransyiration，ET0 was calculated from daily ground-based agro-meteorological data substituted into the Penman-Monteith equation (Allen PG 1998)

● The Formula for calculating LTPP is as follows:When ym≥20kg/ha/h，

YT=cL·cN·cH·G·[F(0.8+0.01ym)y0+(1-F)(0.5+0.025ym)yc]when ym＜20kg/ha/h，

YT=cL·cN·cH·G·[F(0.5+0.025ym)y0+(1-F)(0.05ym)yc]

f(p) =１ P＞ETm1－Ky×(1－P/ETm) P＜ETm

Main parameters of AEZ modelSymbol Definition Values

cL correction crop development and leaf area 0.5

cN correction for dry matter production, 0.6 for cool and0. 5 for warm conditions 0.6

cH correction for harvest index 0.45G total growing period (days) CalculatedF fraction of the daytime the sky is clouded. Calculated

ym maximum leaf gross dry matter production rate of acrop for a given climate, kg/ha/day Calculated

y0gross dry matter production of a standard crop for agiven location on a completely overcast (clouded) daykg/ha/day

, Calculated

ycgross dry matter production rate of a standard cropfor a given location on a clear (cloudless) daykg/ha/day

, Calculated

ky yield response factor 1.25kc crop coefficient 0.825

Reference: Doorenbos J, AH Kassam (1979) Crop Yields Response to Water. FAO Irrigation and drainage paper No. 33. Food and Agriculture Organization of the United Nations, Rome

The climate change during last 30 years in Heilongjiang province

3 Results

Temporal Change

* p < 0.05;** p < 0.1.

The tendency rate of mean temperature and cumulated precipitation

Mean temperature (℃ per decade)

cumulated precipitation (mm per decade)

Annual 0.55* -23.1**

Maize growing season (May.-Sep.) 0.42* -27.6**

Spring (Mar.-May.) 0.53* 5.62 Summer (Jun.-Aug.) 0.38* -25.09**

Autumn (Sep.-Nov.) 0.45* -12.86*

Winter (Dec.-Feb. of next year) 0.76* 1.23 January 0.86 1.41

February 0.76 0.44 March 0.59 3.43*

April 0.51 -0.60 May 0.53* 1.93 June 0.45 -1.04 July 0.31 -2.84

August 0.17 -14.26 September 0.69* -11.41*

October 0.77* -1.41 November 0.08 -0.37 December -0.02 1.05

Spatial Change

The performance of FAO-AEZ model for regional simulation

LTPP and CPP of Maize in Heilongjiang province from 1980 to 2009

The impact of climate change on maize potential productivity

linear linear

Response of LTPP and CPP to future climate change scenarios

Simulated LTPP and CPP responses to different climatic scenarios in future

Scenarios Temperaturincrease(℃

e )

Precipitation decrease(%)

LTPP increase(%)

CPP decrease(%)

Scenarios1 0.5 5 7.5 5.0

Scenarios2 1.0 10 13.7 8.1

Scenarios3 1.5 15 23.1 8.7

4 Discussion and Conclusion

Our analysis of climate-change impacts in maize potential productivity only consider daily mean temperature and precipitation change scenario. Other factors will be considered in next studies.

The outcome of this presentation will be used to analyze the contribution rate of climate change to maize production formation. The preliminary research result showed that the contribution rate of climate change is lesser.

Discussion

The climate was becoming warm-dry in maize growth period in Heilongjiang province from 1980 to 2009

The LTPP increased with the increasing trend of mean temperature, and the CPP decreased with the decreasing trend of precipitation

The water is the main restricted factor to the maize potential productivity of Heilongjiang province. If the water is enough, the climate warming has positive contribution to the maize production

Conclusion