Acknowledgements
We acknowledge the 2015 Young Scientists Summer Program of IIASA, Austria and Professor Lex Bouwman in Utrecht University for supporting.
Conclusion
EPIC performed well in simulating grain yields,
SOC and SAP of different crops under various long- term fertilizer management in China.
Crop yields, SOC and SAP are sensitive to P
fertilizer input and show linear increase followed by reaching plateau. Interestingly, SAP will accumulate in soil finally.
EPIC has great potential to simulate crop growth, SOC and soil P dynamics in China.
Objective
The objectives of this study are to:
calibrate and validate the Environmental Policy
Integrated Climate (EPIC) model (crop yield, soil organic carbon and soil P dynamics)
test the sensitivity of crop yield, soil organic carbon
(SOC) and soil available P (SAP) to varying fertilizer P application rates and meteorological conditions.
Introduction
Phosphorus (P) is a major limiting nutrient for plant growth and thus essential for food security. P is a nonrenewable resource and it controls freshwater eutrophication..
It is thus essential to find an integrated and effective approach to optimize phosphorous fertilizer application in the agro-ecosystem while maintaining crop yield and minimizing environmental impacts.
Many models have been developed to simulate soil C and N, but few consider the long-term dynamics of soil P. Hitherto, calibration/validation of soil P models with long-term field experimental data to study the dynamics of soil P is rare and has not been reported in China .
Method
The Environmental Policy Integrated Climate (EPIC) process-based model was employed to simulate grain yield, SOC and SAP based on 8 field experiments in China with 11 years of data, for 4 treatments: control (CK), NPK fertilizer, NK fertilizer and NPK+manure (NPKM). The sensitivity of soil P to variation of fertilizer P application rates and climate (humid, dry) was also conducted.
Results
Modelling crop yield, soil organic C and P
under various long-term fertilizer management in China
Jie Zhang 1,2 , Guang Xu, Minggang Xu, Juraj Balkovič, Ligia B. Azevedo, Rastislav Skalský, Jinzhou Wang, Chaoqing Yu
1 Tsinghua University, China; 2 Utrecht University, Netherlands zhangle544@gmail.com
Fig 3. Temporal
variation of grain yields:
measure values and model simulation
Fig 2. Sampling locations of the National Soil Fertility and Fertilizer Effects Long- term Monitoring Network and the P fertilizer
application of every province in China in 2013
Table 1. Data used in this work
Fig 1. Phosphorus pools and flows of EPIC model
Data set Description
Climate Daily meteorological data
Soil Soil physical and chemical data Crop management Crop rotation, planting/harvest, tillage, fertilization, irrigation etc.
Crop yield, SOC
and SAP Field observed yield, SOC and SAP
0 6000 12000
CP GZL
Grain Yield (kg/ha)
Observed Modelled
NPK CK NK NPKM
0 5000 10000 0 5000 10000
YL Urum
0 4500 9000
0 6000 12000
Grain Yield (kg/ha)
ZZ
HZ
0 4500 9000 0 4500 9000
BB
1990 1995 2000 0
3500 7000
1990 1995 2000 1990 1995 2000 1990 1995 2000
QY
0 20 40
NPK
CP
Observed Modelled
GZL
CK NK NPKM
0 20 40 0 20 40
Urum
YL
0 20 40
0 2 4 6 8 10
SOC (t/ha)
0 20 40
SOC (t / ha)
QY HZ
0 20 40 0 30 60
BB ZZ
1990 1995 2000 0
20 40
1990 1995 2000 1990 1995 2000 1990 1995 2000
10 20 30 40 50
10 20 30 40 50
y = 0.91x+1.71 R2 = 0.80, n = 180 Calibration
Simulated SOC (t / ha)
Measured SOC (t / ha)
10 20 30 40 50
10 20 30 40 50
y = 0.78x+753.89 R2 = 0.68, n = 329 Validation
Simulated SOC (t / ha)
Measured SOC (t / ha)
Fig 5. The correlation of the simulated and measured SOC and grain yields of all treatments
Simulated P
Reported P
Average Simulated P
Fig 6. Temporal variation of soil available P (mg/kg) : measurements and simulation for site Yangling (NPK) and Zhengzhou (NPK)
Fig 7. Sensitivity of grain yield, annual average SOC and soil available P to sequential P fertilizer
Fig 4. Temporal
variation of grain SOC:
measure values and model simulation
