博士論文

Optimizing Productivity and Quality of Wheat through Modelling Approach(モデリングアプローチによるコムギの生産性と品質の最適化)

A Thesis Submitted to the
Graduate School of Agricultural and Life Sciences, the University of Tokyo
in Partial Fulfillment of the Requirements for the Degree ofDoctor of Philosophy

By
サルパドールヘワワサム

ヌワン

プリヤシャンタ

デ

Salpadoru Hewawasam Nuwan Priyashantha de Silva

2018
The University of Tokyo, Japanシルワ

Dedicated to my loving family and teachersACKNOLEDGEMENTFirst of all, I would like to express my great appreciation to my supervisor Prof. Kensuke Okada
for all the advice, guidance and encouragement over the past five years. He has been a wealthof knowledge and support throughout my research. Without him this research would not havebeen possible.
Besides my advisor, I would like to thank the rest of my thesis evaluation committee: Prof.
Kazuhiko Kobayashi and Prof. Masaru Mizoguchi, Prof. Takashi Yamakawa, and Prof. Junko
Yamagishi for their encouragement, insightful comments.
I am grateful to Prof. Taro Takahashi for his guidance and unfailing support that immensely
helped to make this study a success.
I wish to express my deepest thanks to Prof. Akihiko Kamoshita, Prof. Mariko Norisada, andProf. Takeshi Sato, for providing me the facilities for the experiments, and advising me oncomputer programming.
I am very much thankful to all the staff members and the graduate students at ISAS, Tanashi
for their unfailing support extended for my field experiments.
I am very much thankful to all the professors, lecturers, staff members of the Department of
Global agricultural Sciences for their advices, guidance and support extended.
I would like to express my sincere thanks to Dr. Neelam Ramaiah and staff of the OICE, staffof the graduate student section and staff of the international student advisory room for theirsupport and advises provided.
Thanks to everyone in the IPADS group, Laboratory of Agricultural Development Studies
specially Ms. Noriko Fujimto, and all my friends in Japan and Sri Lanka for being such nicefriends, colleagues and for helping me in numerous ways throughout my studies.

iiWithout the love and blessings of my parents and parents-in-low I could not have carriedthrough this work at all. They were always supporting me and encouraging me with their best
wishes.
I am grateful to my teachers, in Sri Lanka who showed me the path to the success.
My utmost thank, however, is to my loving wife Samanthika who was always there cheering
me up and stood by me through the good times and bad. I appreciate my son, for the very
positive and understanding nature he demonstrated on my career responsibilities.
Last but not least, I extend my deepest gratitude to the University of Tokyo for providing me
financial support with the university fellowship.

S.H. Nuwan Priyashantha de Silva

iii

TABLE OF CONTENTS

LIST OF TABLES

xiii

LIST OF FIGURES

xv

ABSTRACT

xix

ABBREVIATIONS

xxiii

Chapter 1............................................................................................................................. 1
Introduction ...................................................................................................................... 1
1.1 Wheat (Triticum aestivum) the second most important cereal crop in Japan ................ 1
1.2 Self-sufficiency and government intervention for improving wheat production in
Japan .............................................................................................................................. 1
1.3 Nitrogen management to achieve yield gaps and GPC goals ........................................ 3
1.4 Importance of soil type .................................................................................................. 4
1.5 Importance of sowing time ............................................................................................ 5
1.6 Importance of a decision support for wheat cultivation................................................. 6
1.7 APSIM (The Agricultural Production Systems Simulator) ........................................... 9
1.7.1 An overview .......................................................................................................... 9
1.7.2 APSIM-Wheat module.......................................................................................... 9
1.8 Uses of APSIM model in wheat crop management studies including nitrogen
management ................................................................................................................. 11iv

1.9 The purpose of the study .............................................................................................. 13

Chapter 2........................................................................................................................... 15
Effect of sowing time and N application at flowering on grain yield and
GPC of hard and soft wheat grown in volcanic ash soils in Kanto area ... 15
2.1 Introduction .................................................................................................................. 15
2.2 Materials and methods ................................................................................................. 17
2.2.1. Experimental site ............................................................................................... 17
2.2.2. Plant materials .................................................................................................... 18
2.2.3. Experiment 1: Effect of N application at flowering time (2010–2011 and 2011–2012) ............................................................................................................................ 19
2.2.4. Experiment 2: Interaction effects of varieties, sowing dates, and fertilizer
application rates (2012–2013)...................................................................................... 20
2.2.5. Statistical Analysis ............................................................................................. 21
2.3 Results .......................................................................................................................... 22
2.3.1. Weather conditions ............................................................................................ 22
2.3.2. Experiment 1: Effect of N application at flowering time (2010–2011 and 2011–2012) ............................................................................................................................ 24
2.3.3. Experiment 2: Interaction of variety, sowing date, and fertilizer application (2012–2013) ............................................................................................................................ 27
2.4 Discussions .................................................................................................................. 36
2.4.1. The optimal sowing time was confirmed to be early-to-mid November ........... 36v

2.4.2. Delayed sowing decreased the grain yield but increased the GPC .................... 36
2.4.3 The N application at flowering time to control GPC is effective only for optimal
sowing .......................................................................................................................... 37
2.4.4. Soil factors in the context of quantity of N to be applied at flowering time...... 39
2.4.5. Efficacy of N application at flowering time is different between hard and soft
wheat varieties ............................................................................................................. 40
2.4.6. Adoption of cultivars from other regions that perform well under late-sowing
conditions ..................................................................................................................... 41
2.5 Conclusions .................................................................................................................. 42

Chapter 3........................................................................................................................... 43
Effects of late sowing on yield, yield components and seed emergence at
low temperature for winter wheat in Kanto area.............................................. 43
3.1 Introduction .................................................................................................................. 43
3.2 Materials and Methods ................................................................................................. 44
3.2.1 Experimental site ................................................................................................ 44
3.2.2 Plant materials ..................................................................................................... 44
3.2.3 Experimental procedures .................................................................................... 44
3.2.3.1 Experimental design and treatments ............................................................ 44
3.2.3.2 Crop management ........................................................................................ 45
3.2.3.3 Plant measurements ..................................................................................... 45vi

3.2.3.4 Soil measurements ....................................................................................... 46
3.2.3.5 Analysis on the effect of temperature on plant emergence .......................... 47
3.2.4 Statistical analysis ............................................................................................... 47
3.3 Results .......................................................................................................................... 48
3.3.1 Weather conditions ............................................................................................. 48
3.3.2 Grain yield and yield components ...................................................................... 51
3.3.3 Plant emergence .................................................................................................. 56
3.3.3.1 Germination, the number of plants both at maximum emergence and in March...................................................................................................................................... 59
3.3.3.2 Relationship between the number of days to emergence and the number of plants
at maximum emergence ............................................................................................... 61
3.4 Discussion .................................................................................................................... 62
3.5 Conclusions .................................................................................................................. 67

Chapter 4........................................................................................................................... 68
Calibration and validation of APSIM crop model for standard sowing
conditions in Kanto area, Japan .............................................................................. 68
4.1 Introduction .................................................................................................................. 68
4.2 Field experiment data used for model parameterization and validation ...................... 70
4.3 Model Calibration and parameterization ..................................................................... 71
4.3.1 Materials and methods ........................................................................................ 71vii

4.3.1.1 Model initialization ...................................................................................... 71
4.3.1.2 Deriving cultivar parameters........................................................................ 72
4.3.2 Results of model calibration ............................................................................... 74
4.3.2.1 Weather data ................................................................................................ 75
4.3.2.2 Soil data ....................................................................................................... 76
4.3.2.3 APSIM model parameterization .................................................................. 77
4.3.2.3.1 Parameterization for phenology ............................................................ 77
4.3.2.3.2 Parameterization for the production (grain yield, dry matter production,and leaf area index) .............................................................................................. 77
4.4 Model validation .......................................................................................................... 79
4.4.1 Materials and methods ........................................................................................ 79
4.4.1.1 Model initialization ...................................................................................... 79
4.4.1.2 Methods to obtain the simulated data ...................................................... 79
4.4.1.3 Methods of model evaluation....................................................................... 79
4.4.1.3.1 Comparison of observed and simulated values ..................................... 79
4.4.1.3.2 Quantifying the model performances.................................................... 80
4.4.2 Results (Model validation) .................................................................................. 82
4.4.2.1 Validation for phenology ............................................................................. 82
4.4.2.2 Validation results for grain yield, GPC, dry matter production and LAI(optimum sowing) .................................................................................................... 84
4.4.2.3 Model performances for estimating soil nitrate concentration .................... 85viii

4.4.2.4 Model performances for grain yield, GPC, dry matter production and LAI(across all sowing conditions including early and late sowing) .............................. 85
4.5 Discussion .................................................................................................................... 95
4.5.1 Model calibration ................................................................................................ 95
4.5.2 Model validation ................................................................................................. 98

Chapter 5......................................................................................................................... 101
Model improvement to simulate wheat growth under late sowing and thevalidation of the APSIM model across optimum and late sowings ........... 101
5.1 Introduction ................................................................................................................ 101
5.2 Materials and Methods ............................................................................................... 103
5.2.1 Model improvement to change plant density according to the time to emergence.................................................................................................................................... 103
5.2.1.1 Modification of algorithm .......................................................................... 104
5.2.2 Testing and revalidation of the improved model .............................................. 106
5.2.2.1 Testing of the improved model .................................................................. 106
5.2.2.2 Re-validation of the APSIM model after the improvement ....................... 106
5.3 Results ........................................................................................................................ 107
5.4 Discussion .................................................................................................................. 115
5.4.1 Validation across the sowing groups was success ............................................ 115

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5.4.2 Model validation results compared with the existing literature to show theacceptance of the validation ....................................................................................... 115
5.4.3 Model development approach compared with the literature............................. 117
5.4.4 Recommendations for farmers .......................................................................... 118
5.4.5 Research needs for the validation and extension of the model to adjust the
emergence percentage under low temperature conditions ......................................... 118
5.5 Conclusions ................................................................................................................ 119

Chapter 6......................................................................................................................... 120
Scenario analysis for economically optimum nitrogen management for
Kanto region, Japan ................................................................................................... 120
6.1 Introduction ................................................................................................................ 120
6.2 Materials and methods ............................................................................................... 121
6.2.1 Simulation experiment for obtaining optimum nitrogen management ............. 121
6.2.2 Economic optima for the nitrogen application.................................................. 122
6.2 Results ........................................................................................................................ 124
6.3 Discussion .................................................................................................................. 138
6.4 Conclusions ................................................................................................................ 142

xChapter 7......................................................................................................................... 143
APSIM model application to Hokkaido region: parameterization,
validation and scenario analysis for economically optimum nitrogen
management .................................................................................................................. 143
7.1 Introduction ................................................................................................................ 143
7.2 Materials and methods ............................................................................................... 144
7.2.1 Model calibration and validation ...................................................................... 144
7.2.2 Scenario analysis to derive N management optima .......................................... 146
7.2.2.1 Simulation experiment for obtaining optimum nitrogen management ...... 146
7.2.2.2 Economic optima for the nitrogen application........................................... 147
7.3 Results ........................................................................................................................ 148
7.3.1 Model calibration and validation ...................................................................... 148
7.3.2 Scenario analysis to derive N management optima .......................................... 154
7.4 Discussion .................................................................................................................. 167
7.4.1 APSIM model calibration and validation of wheat growth in Hokkaido ......... 167
7.4.2 Economic optima for the nitrogen application in Hokkaido ............................. 169
7.5 Conclusions ................................................................................................................ 172

xiChapter 8......................................................................................................................... 173
General Discussion and Conclusions ................................................................... 173
8.1 N management optima elucidated from modeling to improve the wheat productivity.................................................................................................................................... 174
8.2 Implications of modelling approach on uplifting current yield levels ....................... 175
8.3 Conclusions ................................................................................................................ 177

References ................................................................................................................... 179

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LIST OF TABLES

Table 1.1 Quality bonus offered based on the GPC .................................................................. 2
Table 1.2 Use of simulation models as a decision support ....................................................... 8
Table 2.1Soil characteristics ................................................................................................... 18
Table 2.2 Rate of nitrogen fertilizer application (kg N ha-1) ................................................... 19
Table 2.3 Effect of N application rate (split) and sowing time on the grain yield, GPC, total
dry weight at maturity, and harvest index in 2010–2011 and 2011–2012 ............................... 25
Table 2.4 Effect of N application rate (split), variety, and sowing time on the dry matterproduction at flowering and maturity, and the LAI in 2012–2013 .......................................... 32
Table 2.5 Effect of N application rate (split), variety, and sowing time on the grain yield, GPC,
and yield components in 2012–2013 ....................................................................................... 33
Table 3.1 Results of ANOVA for grain yield, GPC, dry matter production and yieldcomponents. ............................................................................................................................. 52
Table 3.2 Grain yield, GPC, dry matter production and yield components for Ayahikari (a.)
and Yumeshiho (b.) in 2014-2015 and 2015-2016. ................................................................. 53
Table 3.3 Correlation coefficient among grain yield and yield components. The data of all N
treatments for two varieties in two seasons were used for the analysis. .................................. 56
Table 4.1 Summary of the field experiments used in the model calibration and validation ... 71
Table 4.2 Soil nitrogen and pH status at the time of sowing and Fbiom and Finert values
obtained after the adjustment to comply with zero nitrogen treatment observed values (for 010 and 10-40 and 40-70 soil layers) ......................................................................................... 76
Table 4.3 Phenological parameter values obtained for both cultivars .................................... 77
Table 4.4 Adjusted parameter values for each cultivar to receive better simulation results for
LAI, dry matter production and grain yield. ............................................................................ 78xiii

Table 4.5 Summary of APSIM model performances for Kanto area (Volcanic ash soils) inJapan for Phenology across all sowing groups of 2014-2015 and 2015-2016 ........................ 93
Table 4.6 Summary of APSIM validation for Kanto area in Japan for growth parameters. The
results of two different analysis were compared (one with the data at optimum conditions only,
the other with the data from all sowing groups). ..................................................................... 94
Table 5.1 Adjusted parameter values for parameter – leaves per node (only for Yumeshiho)................................................................................................................................................ 107
Table 5.2 Model performances before and after model improvement across all sowing groups(including early, optimum and late sowing) .......................................................................... 114
Table 6.1 Parameters used for calculation ............................................................................ 123
Table 6.2 Fertilizer application guidelines for wheat in Kanto region.................................. 131
Table 7.1 Description of the experimental data used for model calibration and validation . 145
Table 7.2 Description of soil data ......................................................................................... 146
Table 7.3 Cultivar parameter values obtained for both cultivars .......................................... 148
Table 7.4 Summary of the APSIM model performances for Hokkaido region .................... 153
Table 8.1 Percentage change in median gross margin due to optimal N treatment for hard andsoft wheat grown in Kanto and Hokkaido regions................................................................. 175

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LIST OF FIGURES

Figure 2.1 Monthly average of daily maximum and minimum temperatures and monthly
precipitation for three cropping seasons of the experiment, and their 30-year average at theTokyo Meteorological Station, Tokyo, Japan. ......................................................................... 23
Figure 2.2 GPC response of the soft wheat variety (Ayahikari) and the hard wheat variety(Yumeshiho) to different N treatments at different sowing times ........................................... 28
Figure 2.3 Effect of the sowing time on the date of flowering and physiological maturity, andthe duration between sowing, flowering time, and physiological maturity for different wheat
varieties .................................................................................................................................... 29
Figure 2.4 Grain yield and GPC response of the soft wheat varieties (Ayahikari and
Nebarigoshi), and the hard wheat varieties (Yumeshiho and Nishinokaori) to different N
treatments at different sowing times. ....................................................................................... 31
Figure 2.5 Grain yield-protein relationship for different N treatments ................................... 38
Figure 3.1 (a.) Monthly average of daily minimum, maximum and average temperatures and,monthly rainfall, in 2014-2015 and 2015-2016 cropping seasons. (b.) Changes in daily
minimum, maximum and average temperatures from December to March in 2014-2015 and2015-2016. (c.) Daily average air temperature and soil temperature at 5 cm depth from 2014
December to 2015 February..................................................................................................... 50
Figure 3.2 Grain yield and yield components under N160 treatment for Ayahikari (a-d) andYumeshiho (e-f) in 2014-2015 and 2015-2016. ...................................................................... 55
Figure 3.3 Changes of emergence percentage for Ayahikari (a-d) and Yumeshiho (e-h) in
2014-2015. ............................................................................................................................... 57
Figure 3.4 Changes of emergence percentage for Ayahikari (i-l) and Yumeshiho (m-p)
varieties in 2015-2016.............................................................................................................. 58
xv

Figure 3.5 Relationship between number of days to emergence and average temperature during
the emergence period (TAVG; 0C) for Ayahikari and Yumeshiho varieties, in 2014-2015 and2015-2016. ............................................................................................................................... 59
Figure 3. 6 Expected number of plants at germination, the observed number of plants at
maximum emergence and in March (m-2) for Ayahikari and Yumeshiho ............................... 60
Figure 3.7 Relationship between maximum emergence percentage and rate of emergence .. 61
Figure 3.8 Aerial view of plant stand of the 2014-2015 experiment under each sowing group.
.................................................................................................................................................. 64
Figure 4.1 Daily maximum and minimum temperature and rainfall averaged over a month..
.................................................................................................................................................. 75
Figure 4.2 The equations for calculating RMSE and ME ....................................................... 81
Figure 4.3 Simulated and observed number of days to flowering and maturity in 2014-2015
and 2015-2016 for Yumeshiho (a-b) and Ayahikari (c-d). ...................................................... 83
Figure 4.4 Simulated and observed grain yield, GPC and dry matter production at floweringand maturity, and LAI for Ayahikari (a,b,c,d,e) and Yumeshiho ( f,g,h, i,j) in 2014-2015, and2015-2016 at optimal sowing................................................................................................... 87
Figure 4.5 Simulated and observed grain yield in response to two different N treatments for
Ayahikari (a-b) and Yumeshiho (c-d). ..................................................................................... 88
Figure 4.6 Simulated and observed GPC in response to two different N treatments for
Ayahikari (a-b) and Yumeshiho (c-d). ..................................................................................... 89
Figure 4.7 Simulated and observed grain yield, GPC and dry matter production at floweringand maturity, and LAI for Ayahikari (a,b,c,d,e) and Yumeshiho ( f,g,h, i,j) in 2014-2015, and2015-2016 for all sowing groups.. ........................................................................................... 91
Figure 4.8 Simulated and observed soil NO3- during the 2014-2015 and 2015-2016 cropping
seasons. .................................................................................................................................... 92

xvi

Figure 5.1 Simulated and observed grain yield, GPC and dry matter production at floweringand maturity, and LAI for Ayahikari, in 2014-2015, and 2015-2016 for all sowing groups
before (a,b,c,d,e) and after ( f,g,h,i,j) model improvement across all sowing groups. .......... 111
Figure 5.2 Simulated and observed grain yield, GPC and dry matter production at floweringand maturity, and LAI for Yumeshiho, in 2014-2015, and 2015-2016 for all sowing groups
before (k,l,m,n,o) and after ( p,q,r,s,t) model improvement across all sowing groups. ......... 113
Figure 6.1 Variation of grain yield (a), GPC (b) and gross margin (c) of each N treatment for
Yumeshiho. ............................................................................................................................ 126
Figure 6.2 Variation of grain yield (a), GPC (b) and gross margin under (c) each N treatment(N1-N64) for Ayahikari. ........................................................................................................ 127
Figure 6.3 Variation of grain yield, GPC and gross margin with the amount of N applied. 129
Figure 6.4 Variation of grain yield, GPC and, gross margin at the lowest (0_0_0), farmers’
practice (80_20_0), optimum (280; 120_120_40 / 120_80_80) and, maximum (120_120_120)
N levels for Ayahikari and Yumeshiho.. ............................................................................... 134
Figure 6.5 Change in optimal N application rate over the 55 years and respective grain yields,
GPCs and gross margins of Yumeshiho (a) and Ayahikari (b). ............................................ 137
Figure 7.1 Simulated and the observed number of days to flowering and maturity, grain yield
and GPC of Kitahonami (a-c) and Yumechikara (d-f)........................................................... 150
Figure 7.2 Simulated and the observed grain yield and GPC of Kitahonami (a-c) andYumechikara (d-f) at different N application treatments. ...................................................... 151
Figure 7.3 Variation of grain yield (a), GPC (b) and gross margin (c) of each N treatment for
Kitahonami. ............................................................................................................................ 157
Figure 7.4 Variation of grain yield (a), GPC (b) and gross margin (c) of each N treatment for
Yumechikara. ......................................................................................................................... 158
Figure 7.5 Variation of grain yield, GPC and gross margin with the amount of N applied. 160

xvii

Figure 7.6 Variation of grain yield, GPC and, gross margin at the lowest (0_0_0), farmers’
practice (40_100_40 / 40_90_60), optimum (120_40_0 / 120_120_40) and, maximum(120_120_120) N levels for Kitahonami and Yumechikara. ................................................. 164
Figure 7.7 Change in optimal N application rate over the 30 years and respective grain yields,
GPCs and gross margins of Kitahonami (a) and Yumechikara (b). ...................................... 166
Figure 8.1 National average wheat grain yields (data points with close circles) Vs. Fertilizer
application rates in Japan, France, Netherlands, New Zealand and United Kingdom and mediangrain yields of hard and soft wheat cultivars (data points with open circles) from Kanto and
Hokkaido regions, resulted from optimal N schemes . .......................................................... 176

xviii

ABSTRACTPh.D Thesis submitted to the Department of Global Agricultural Sciences,
Graduate School of Agriculture and Life Sciences,
The University of Tokyo
Salpadoru Hewawasam Nuwan Priyashantha De Silva
Supervisor: Prof. Kensuke Okada

Optimizing Productivity and Quality of Wheat through Modelling Approach(モデリングアプローチによるコムギの生産性と品質の最適化)

Introduction
Wheat is the second most important cereal crop in Japan. However, the national average
wheat yield in Japan (4.1 t ha-1) is lower than that in other major wheat producing countries. In
the last decade, per capita consumption of wheat increased by 4.1% while that of the staple
food rice decreased by 11%. The self-sufficiency ratio of wheat in Japan is only 13.3%.
Therefore, in order to safeguard the food security and maintain agricultural sector, Japanese
farmers are subsidized for wheat production through “quality bonus” depending on the grain
quality indices including grain protein content (GPC). GPC can be controlled by nitrogen (N)fertilizer management, but it is the most variable factor depending on the soil and climaticconditions and the management practices. Therefore, it is an important matter at both nationaland farm level to improve the N management of wheat to increase grain yield by maintaining
adequate GPC.
Modelling approach with field experiments and proper validation would be useful topredict the yield and GPC under different management options including N application and toprovide necessary decision support for the producers to improve wheat productivity.
The present study focused on first, evaluating the interactive effects of nitrogen
application at flowering and sowing date on grain yield and GPC of hard and soft wheat
varieties, and elucidating the mechanisms of yield and GPC change under late sowing
xixconditions. Second, integrating that knowledge for the parameterization of two Japanesecultivars (hard and soft wheat) representative of Kanto region in Japan for APSIM (Agricultural
Production Systems sIMulator) crop growth model, and validating the APSIM model for theconditions in Kanto area for the optimal sowing conditions and improving the model for latesowing conditions. Third, developing a decision support on optimum nitrogen management
using the validated model to improve the wheat productivity for Kanto region. And fourth,
extending the same modelling approach to Hokkaido region.
Materials and methods
To study the effect of nitrogen application at flowering time and sowing timeinteractions three field experiments were conducted from November 2010 to June 2013, at theInstitute for Sustainable Agro-Ecosystem Services (ISAS) of the University of Tokyo at
Nishitokyo City, Tokyo. The factors of the first experiment were 2 sowing dates and 4 N
application level. Those of the second experiment were 1 sowing date and 4 N application level.
Those of the third experiment were 4 sowing dates, 3 N application level. N was applied asbasal and two splits (split at the time of stem elongation and flowering) in all these experiments.
Two hard wheat cultivars, Yumeshiho and Nishinokaori, and two soft wheat cultivars
Ayahikari and Nebarigoshi were used for the 2012-2013 experiment and only Yumeshiho andAyahikari were used for the other two experiments.
Further, to elucidate the mechanisms of yield and GPC change under late sowing
conditions, two field experiments were conducted from 2014 to 2016 at the same location. The
cultivars Yumeshiho and Ayahikari were used. The 4 sowing dates including very late sowing
and 2 levels of N application were tested.
Phenology, dry matter production, LAI, grain yield and GPC data from 2012-2013 field
experiment were used to parameterize the APSIM model for Ayahikari and Yumeshiho. Then,APSIM model was validated for phenology, dry matter production, LAI, grain yield and GPC
and, soil NO3 using independent experimental data from 2010-2016. Further, APSIM model
was validated for a hard and a soft wheat cultivars representative to Hokkaido using soil andclimatic data of the area and crop experimental data from the literature.
APSIM model was improved to be applied under late sowing conditions by integrating
an empirical model developed from plant emergence data into APSIM model. Then thexximproved model was revalidated with data set including both optimum and late sowing
conditions.
Scenario analysis were conducted with validated model with 64 N treatments ranged
from 0 – 360 kg total N ha-1 with different proportion of basal and split applications at standard
sowing date by using 55 years daily weather data of Tokyo for the wheat cultivars Yumeshiho
and Ayahikari in Kanto region. Similar scenario analysis was conducted for two Hokkaido
cultivars, Yumechikara (hard wheat) and Kitahonami (soft wheat), using 30 years weather data
to find out economically optimal N application regime.
Results and Discussion
The results of the field experiments suggested that the N management strategy atflowering time for increasing the GPC is suitable under optimum sowing conditions, and the
GPC content of both hard and soft wheat sown at optimum timing can be adjusted for fitting
into the quality bonus window, by altering the fertilizer application rate at flowering time. The
study contributes toward improving our understanding of the effects of split N fertilizer
application at the stem elongation and flowering stages, and the effect of sowing time on thegrain yield and GPC of both hard and soft wheat grown in volcanic ash soils. This was the firststudy to report the interactive effect N application at flowering and sowing time for both hardand soft wheat varieties grown in volcanic ash soils.
We demonstrated that the late sowing conditions decreased grain yield and increased
GPC of both hard and soft wheat grown in Kanto region. Grain yield reduction by late sowing
was mainly caused by reduced number of heads per area, which was mainly caused by the
reduced emergence. The lower temperature which the late sown seeds has to face not only
delayed the emergence but also reduced the ultimate emergence percentage. The reduction in
the ultimate emergence percentage was quantitatively linked to the time required from thesowing to emergence with quadratic function.

Model validation confirmed that parameters derived from the calibration were accurate
and these parameters can be used for the prediction of wheat phenology and growth parameterssuch as yield and GPC. The results showed that RMSE for grain yield was 79.9 and 69.0 g m 2

for Ayahikari and Yumeshiho, respectively and that for GPC was 1.8 and 2.4 %. Similarly,

validation results for Hokkaido for optimal sowing showed that RMSE for grain yield was 75
and 86 g m-2 for Kitahonami and Yumechikara, respectively, and that for GPC was 1 and 0.5 %.
xxi

These results are within the range of RMSE in the APSIM model validation studies reported
elsewhere.
The results of the scenario analysis indicated that economically optimum rate of total
N application for Yumeshiho (hard wheat) and Ayahikari (soft wheat) in Kanto region wasboth 280 kg N ha-1 although the proportion of the split application was different. With these
optimum N treatments, the producers can obtain by 85 and 146 % higher median gross margin
for Yumeshiho and Ayahikari, respectively, compared to that can be obtained with current
fertilizer recommendations. The simulation results showed that optimal N application scheme
tended to fluctuate to some extent from year to year due to the climate variation. However inthe years with normal precipitation it was at or closer to the optimal N scheme.
In the scenario analysis in Hokkaido, the economically optimum rates of total N
application for Yumechikara (hard wheat) and Kitahonami (soft wheat) were 280 and 160 kg
N ha-1 respectively. With these optimum N treatments, it was simulated that the producers canobtain by 29 and 20.5 % higher median gross margin for Yumechikara and Kitahonami,
respectively. The simulation results showed that yearly optimal N application scheme tended
to be relatively stable during the 30 years’ period.
Conclusions
The purpose of this study was to optimize productivity and grain protein content of
wheat through modelling approach. N management was focused as the main agronomical
option. The strategy to cope with the late sowing issue was also tested with the same approach.
It was demonstrated that, by increasing total rate of N application, current yield level region
can be increased particularly in Kanto region to the level similar to that of other high yielding
countries with reasonably appropriate GPC, and thereby profitability of the producers could be
increased. This is the first study to use APSIM model for wheat management in Japan. The
section of the model improvement also contains originality, with which the model better fit to
the late sowing situation by incorporating the algorism of reducing the plant number at low
temperature. For the future research, it was suggested to extend and test this modelling
approach to other regions such as Kyushu.

xxii

ABBREVIATIONSANOVA; Analysis of variance
APSIM; Agricultural Production Systems Simulator
GPC; Grain protein content
LAI; Leaf area index
LSD; Least significant difference
ME; Modelling efficiency
N; Nitrogen
RMSE; Root mean square error
RRMSE; Relative root mean square errorxxiii

Chapter 1
Introduction
1.1 Wheat (Triticum aestivum) the second most important cereal crop in Japan
Wheat is the second highest energy source in the diet of Japanese, after rice (FAO,
2014). People consume wheat products such as bread, pasta, Japanese noodles (udon), andChinese noodles (ramen) almost on a daily basis. The per capita consumption of wheat has
been increased in Japan since 1950s with several fluctuations over the time owing to thedeclining happened with the increase in rice production, increased in consumption of animal
products between 1954 and 1957 and between 1981 and early 1990s respectively (Smil andKobayashi, 2012). Lately, per capita, wheat consumption increased by 4.1% in last decade
while that of the staple food rice decreased by 11.1% (USDA, 2017). At present, wheat is
ranked second after maize amongst the import commodities in food and agriculture category,in Japan (FAO, 2014). Although Japan has favourable wheat-growing conditions, with
sufficient annual rainfall of 840–2800 mm, the national average yield in Japan (4.1 t ha-1) islower than that in other major wheat producing countries in Europe, such as France, Germany,
Netherlands and UK (6.8, 8.1, 8.8 and 8.5 t ha-1, respectively). The largest wheat producing
region in Japan is Hokkaido (64.7%) followed by Kyushu (13.8%) and Kanto (8.1%).

1.2 Self-sufficiency and government intervention for improving wheat production inJapan
The self-sufficiency ratio of wheat in Japan is a mere 13.3% (FAO, 2014) and the
production is not sufficient to meet the domestic requirement. Therefore, in order to safeguard
the food security and maintain agricultural sector, Japanese government is implementing a
subsidy system for wheat. Under this subsidy system, producers are given an acreage subsidy
1for each hectare of land that produced wheat (115,220 JPY ha-1) and the quality bonus is given
for the produced wheat. In this quality bonus subsidy system, the producers can receive a
quality bonus payment from the government based on four grain quality parameters, i.e., grain
protein content (GPC), falling number, ash content and bulk density.
Table 1.1 shows the quality bonus scheme offered based on the GPC (MAFF, 2014).
According to this scheme, there are three levels of ranking for quality bonus. Farmers canobtain maximum quality bonus if they can attain the level of GPC required for ranking A. In
other countries as well (e.g. US), the premium prices for wheat are determined by the GPC(Olmos et al. 2003). Wheat varieties in Japan are classified into two groups, i.e., hard wheat(used for bread) and soft wheat (used for noodle), depending on the grain quality (mainly
protein content) (Nakano et al. 2008).

Table 1. 1 Quality bonus offered based on the GPC

Wheat type

Rank

Required GPC (%) range

Hard Wheat

A

11.5 - 14.0

Qualitybonus(¥ t-1)150,000

B

10.0 - 11.5 and 14.0- 15.5

141,667

C< 10.0 and 15.5 <118,667A

9.7 - 11.3

107,500

B

8.5 - 9.7 and 11.3 - 12.5

99,157C> 8.5 and 12.5 <76,333

Soft Wheat2

1.3 Nitrogen management to achieve yield gaps and GPC goals
Among these quality indices, grain protein content (GPC) is the most variable, as it
depends on the soil and climatic conditions as well as the management practices. It has been
widely reported that wheat GPC is influenced by the climate, cultivar, nitrogen (N) application
rate, N application timing, seeding rate, soil fertility, and the interactions among these factors(Lopez-Bellido et al., 2005; Garrido-Lestache et al., 2004; Gauer et al., 1992; Sato et al., 1992;
Rao et al. 1993; Nakano and Morita 2009, Nakano et al. 2008). In addition, there is usually anegative genetic correlation between the grain yield and GPC (Triboi et al., 2006; Asseng and
Milroy, 2006; Kibite and Evans, 1984; Loffler, 1985; Selles and Zenter, 2001; Fowler, 2003;
Triboi and Triboi-Blondel, 2002), which makes it challenging to increase the yield while
maintaining the GPC within the target range (Triboi et al., 2006).
Recent research has attested that the GPC can be controlled by N application at theflowering stage, while N application at the stem-elongation stage affects the grain yield(Nakano and Morita 2008; Woolfolk et al., 2002; Fischer et al., 1993; Yoshida et al., 2008;
Shimazaki and Watanabe, 2010; Takayama et al., 2006; Stark and Tindall, 1992; Zebarth andSheard, 1992; Peltonen, 1993; Bly and Woodard, 2003; Rutkowska, 2009; Knowles et al.,
1994; Rawluk et al., 2000). However, the effectiveness of split N application at flowering for
improving the GPC depends on the soil type, and therefore, the control of GPC solely by split
N application is difficult (Karathanasis et al., 1980; Sato et al., 1992; Nakatsuji, 2003).

3

1.4 Importance of soil type
Despite the availability of the improved cultivars that has a potential to produce grainsof appropriate protein content than standard cultivars still, the expected results cannot be
obtained in the farmers’ field due to the effect of climate variation and soil characteristics. For
example, the grain quality of wheat cultivar Minaminokaori has not yet met the required protein
content as a result of lower nitrogen uptake by the wheat plant due to the shorter growing
season in western part of Japan owing to warmer climate and relatively high precipitation (Taya,
2001). Soils of the western part of the Japan are generally mineral soils and their nitrogen
supplying capacity is low. Therefore, farmers in this area can increase the grain protein content
by a second split application of nitrogen fertilizer.
Volcanic ash soils, one of the major soil types in Japan (Shoji and Takahashi, 2002;
Nanzyo, 2002), are also important soils for wheat production in Japan. Hokkaido, the largest
wheat-growing prefecture in Japan, has a high percentage (40%) of volcanic ash soils (Shoji
and Takahashi, 2002), while the Kanto region, the third-largest wheat growing area, is nearly
composed entirely of volcanic ash soils except alluvial soils along the rivers.
Volcanic ash soils are among the most productive soils in the world for agriculture andforestry, and usually have their colloidal fraction dominated by Al-humus complex or
allophane/imogolite in humid weathering environments (Ugolini and Dahlgren, 2002). Organic
matter tends to be highly accumulated in volcanic ash soils, owing to their stabilization by theformation of Al-humus complex. Non-crystalline materials and humus contribute to the uniquechemical and physical soil properties of volcanic ash soils, such as high water-holding capacity,
variable charge, high phosphorus retention, low bulk density, high friability, and high rate offormation of stable soil aggregates (Soji et al., 1993). Some of these properties support a higher
productivity of crops through a better retention of nutrients and water, compared with othersoil types when the disadvantage, high P fixing capacity, is corrected by higher phosphorus

4application. The native nitrogen-supplying capacity in volcanic ash soils is higher than that inother soils and is further enhanced by the application of N fertilizers, which stimulates themineralization of the native soil nitrogen (Eneji et al., 2002).
Therefore, if the temperature is high enough, soil releases higher amount of nitrogenand coincidence of such condition with the anthesis period of the crop will result in very highgrain protein content. Otherwise, the grain protein content becomes lower. Thus, farmers arefacing a difficult situation in achieving expected grain protein content.

1.5 Importance of sowing time
In the case of winter wheat, the plants sown in the optimal period, better germination
rates, good crop establishment (Farroq et al., 2008), and longer vegetative period can be
expected, together with a better root growth, compared with the late-sowing conditions(Barraclough and Leigh, 1984). However, delayed sowing tends to reduce the grain yield while
increasing the GPC typically (Singh and Jain, 2000). Late planting is a major limitation towheat productivity in many regions of South Asia that have a rice-wheat cropping system(Hobbs et al., 1994) and in other temperate and subtropical countries. For the main target areaof this research, Kanto area in Japan, the interaction between the sowing time and the effect of
fertilization at flowering time on GPC is an area that has not been duly addressed in literatureespecially in relation to the wheat grown in volcanic ash soils.

51.6 Importance of a decision support for wheat cultivation
Understanding the interactions between crop, soil and climate are vital to makedecisions on optimum nitrogen application and other agronomic practices towards achieving
higher yield and appropriate grain protein content for wheat grown under optimal as well aslate sowing conditions. From these reasons, the comprehensive decision support system for theproper nitrogen management is awaited in wheat production in Japan to narrow the existingyield gaps while maintaining expected GPC required for higher quality bonus for wheat sownunder optimal sowing conditions and to minimize the yield losses under late sowing conditions.
So far numerous field experiments have been conducted for the above purposes. But
there are several limitations in such field researches. They are:1. Due to the spatial variability in climate and soil characteristics, the results of filed
experiments are site specific. Existing literature also reported that the results of the field
experiments are season-specific, too (e.g. Spiertz and Ellen, 1978; Spiertz and Van de Haar,
1978; Ellen and Spiertz, 1980; Chaney, 1990; Darwinkel, 1998; as cited in Asseng et al., 2000).

2. Requirement of the longer duration to obtain the reasonable results; multiple year trials areneeded to conclude any fertilizer or crop related recommendations.

3. Multiple experiments with different trial combinations are needed to find out any optimum
conditions (e.g., optimum rate and timing for nitrogen application).

Therefore, use of crop growth models is one of the best alternative options available
and a properly validated crop growth model is a good decision support system for both farmers
and scientists to make economically viable decisions on management practices and research
perspectives. Existing reports also explain the use of crop models to optimise management
practices under variable environments (Van Keulen and Seligman, 1987; Stapper and Harris,

6

1989; Keating et al., 1991; Meinke et al., 1993; Savin et al., 1995; Thornton et al., 1995; Asseng
et al., 1998 as cited in Asseng et al., 2000). But before coming up with any recommendations
based on a crop model, the model should be properly validated for the local condition. APSIM(The Agricultural Production Systems Simulator) is one of such crop growth models that has
successfully been used in many other countries to simulate wheat growth (e.g., Asseng et al.,
2000; Wang et al., 2013; Zang et al., 2012; Mohanty et al., 2012 ; Balwinder-Singh et al., 2011).
Table 1.2 shows the different situations of using simulation models for decision support.

7

Table 1.2 Use of simulation models as a decision support

Type of decision support

Name of theReference

model
Decision support for Australian dry
land farming (commercial crops)

APSIM

Carberry (2009)

Decision
supportforwheatmanagement in subtropical Australia

WHEATMAN

Wood ruff (1992)

Decision support on wheat N
fertilization based on the date of N
deficiency

AzodynJeuffroy and Recous(1999)

ClimateDSSAT

Hoogenboom et al.

changeimpacton

crop

production(1995)

Decision support for irrigation andwater management

CADSM

Prajamwong et al.
(1997)

Wheat disease management

Foliar disease
model

Audsley et al. (2005)

Assessing nitrogen leaching losses
from arable land

SOILNDBJohnsson et al. (2002)

Empirical
model and online
decision support
tool

Gramig et al. (2017)

N application decision support for corn8

1.7 APSIM (The Agricultural Production Systems Simulator)

1.7.1 An overview
APSIM (Agricultural Production Systems sIMulator) is one of the advanced cropping
system model that comprised of different modules enabling to simulate systems that cover arange of plant, animal, soil, climate and management interactions (Keating et al, 2003).

1.7.2 APSIM-Wheat module
Simulation is carried out in a daily time-step on an area basis. Weather (temperature,radiation, precipitation) data should be provided externally. The initial content of water andinorganic nitrogen at different soil layers are required as input and also the crop management
information should be provided for the initialization of the simulation.
Soil water and nitrogen uptake data from the wheat module is disseminated on daily
basis to the soil water and nitrogen modules. Apart from that, information on crop cover istransferred to the water balance module for the calculation of evaporation rates and runoff. At
the time of crop harvest, information on the amount of wheat stover and root residues are passed
to the modules of surface residue and soil nitrogen, respectively. Phenological development,
leaf area growth, biomass and nitrogen concentration of leaves, stems, roots, and grains, grainsize, grain number are simulated in the wheat module on a daily basis.
The phenology is comprised of 11 stages. They are sowing, germination, emergence,
end of juvenile, floral initiation, flowering, start of grain filling, end of grain filling, maturity,
harvest rips, and end crop. The length of each stage is determined by a fixed thermal time
(APSIM wheat module document). Sowing to germination is determined by soil water
availability in seeded layer and germination to emergence phase is comprised of two sub phases

9from which the first has a fixed thermal time and the thermal time for the second is calculatedwith sowing depth and thermal time required for unit elongation.
The daily biomass accumulation is calculated as a potential biomass accumulation,
resulting from radiation interception. This potential biomass accumulation is limited by soil
water deficiency. Radiation interception is calculated from leaf area index (LAI) and the light
extinction coefficient. Radiation use efficiency is a function of growth stages and each growthstage has fixed value of radiation use efficiency.
For the biomass partition, wheat plant is divided into four components. They are root,
head, leaf, and stem. Stem is regarded as uni-clum in APSIM wheat and number of tillers arealso represented by nodes on main stem. Head is divided into grain and pod (correspond to
spike without the grain).
The number of grains per plant is determined by the stem weight at anthesis with acultivar specific value named grains per gram stem. The grain demand is calculated in thegrowth phase from flowering to end of grain filling. The rate of grain filling is affected by dailymean temperature. Grain nitrogen demand starts at anthesis. The original GPC routine
simulated GPC as a function of independent dry matter and N accumulation into the grain.
After Asseng et al. (2002) an upper boundary of daily protein transfer to the grain is set to 4%
N (22.8% GPC), and a lower boundary is set at 1.23% N (7% GPC) to constrain GPC
simulations under very high and very low input conditions.
N stress phase begins before 30% floral initiation to finish at the harvest ripe. N stress
is applied on biomass accumulation, leaf appearance and expansion, and grain filling in thecurrent version of model. N stress on grain filling affects the biomass demand of grain and the
N demand of grain. In the current version of APSIM wheat model, effect of phosphorous stressis not dealt with.

10

1.8 Uses of APSIM model in wheat crop management studies including nitrogen
management
There have been very few reports on the application of crop growth models for wheat
under Japanese conditions (e.g., Kuwata, 2013; Seino, 1995; Honda et al., 2014). No report on
the application of APSIM model under Japanese conditions was published so far. The
literatures on the application of APSIM crop growth model for wheat are, however, found inother countries.
Asseng et al. (1998, 2000) tested the performance of APSIM model in Western
Australia and the Netherlands. Their studies in Western Australia showed that APSIM Nwheat
model was able to simulate the wheat crop growth and yield reasonably. They, however, did
not recommend the version of the model they tested for grain protein studies and therefore
another study was reported later for improving the model for better GPC prediction (Asseng etal., 2002). In the Netherlands, APSIM Nwheat showed better performance to simulate theinteractions between a wide range of nitrogen fertilizer applications and soil nitrogen dynamics,
crop N uptake, crop growth and phenology, grain yield and grain protein content.
Asseng et al. (2000) reported that APSIM simulations appropriately reflected thedifferences in LAI with different N treatments however, LAI was over-estimated between endof tillering and flag leaf stage.
The climate and soil type varies spatially. Thus, wheat yield also changes from place toplace with different crop management practices such as cultivars, sowing time, plant density,and irrigation and fertilizer application. As I mentioned earlier, it is difficult to capture such
effects via field experiments only. Zhang et al. (2012) reported such study using APSIM withthree sowing dates, two to three crop varieties, and three planting densities at three ecologicalsites in the Northern China Plain. Their results showed that the model could capture a larger
part of the variation in phenology, biomass and crop yield for the same variety across the sites.

11But, errors in simulation in phenology and yield were increased with delay in sowing date andwith decreased planting density. According to their study, the model showed a better
performance in a warmer site than a colder site. Another study was reported by Chen et al.
(2010) on capturing the crop productivity responding to inter-annual climate variability andirrigation water supply with long term crop yield data under various irrigation water supply inNorthern China Plain for wheat and maize double cropping systems. Their results show that
APSIM model is capable to simulate growth and yield of wheat and maize in a double cropping
systems. Predictions of soil water and evapotranspiration were also good. They mentioned that
it was necessary to change the low temperature threshold for leaf area damage induced by low
temperature, the temperature response of crop phenologies, and temperature response ofradiation use efficiency (RUE) for the better simulation of the winter wheat production
Further, Asseng et al. (2000) were able to derive the economic and environmental
optima in winter wheat under the Netherlands conditions; N application up to 140 kg N ha−1 inFebruary (period at which crop growth starts after the winter), 90 kg N ha−1 between tilleringand beginning of stem elongation and 40 kg N ha−1 at flag leaf stage resulting in a median of
8.5 t ha−1 grain yield, 14.0% grain protein and 13 kg N ha−1 soil residual N after the harvest.
Therefore APSIM has shown its capacity to be used as a decision support system upon thevalidation.
This is what exactly needed in Japanese wheat production systems. Therefore, in thisstudy field experiments were conducted to understand the effect of N application at flowering
time and sowing time interactions for both hard and soft wheat and to understand themechanism for yield change under late sowing conditions. Then, the data from the field
experiments were used to parameterize and validate the APSIM model for Kanto area in Japan
to conduct scenario analysis with various N application scheme with simulation study to findout the economically and environmentally optimum N application regime for winter wheat.

12

Further, with the use of secondary data (published data) from Hokkaido, the possibility ofapplying similar approach to other regions in Japan were tested.
As reported in the literature, APSIM model performances were not good at late sowing
and under low temperature conditions (Zhang et al., 2012) and therefore, there is a necessityof improving the APSIM wheat model to capture the effect of late sowing and low temperatureon wheat growth by which model prediction to be improved.

1.9 The purpose of the study
The purpose of this study is to develop a modelling approach in combination with field
experimental knowledge and crop modelling to improve the wheat productivity and quality(hard and soft wheat) in Japan. Hokkaido and Kanto regions are selected for the study together
which account for 74.8% wheat production in Japan. Most part of these regions comprised ofvolcanic ash soils which has higher indigenous N supplying capacity. Hence, N management
need to be done carefully to increase the grain yield while maintaining the GPC content atstandard levels. With this background, N management is considered as the main agronomical
technology. In addition to that, the late sowing, which is a prevalent condition in Kanto region
affecting the wheat productivity is also considered to evaluate its effects with N management.
With respect to the thesis outline, Chapters 2 and 3 consist of the knowledge from field
experiments regarding the effect nitrogen application at flowering time and sowing timeinteractions on grain yield and GPC of hard and soft wheat grown in volcanic ash soils and,effects of late sowing on yield, yield components and seed emergence at low temperature forwinter wheat in Kanto region, Japan. Chapters 4 and 5 are about the model application:
parameterization and validation of APSIM model and developing a model improvement forlate sowing conditions for Kanto region. Chapter 6 focuses on scenario analysis using validated
13

model to elucidate economically optimum N management scheme to improve the wheat
productivity in Kanto region, Japan and, Chapter 7 consists of validation of APSIM model for
Hokkaido region and scenario analysis to elucidate economically optimum N management
scheme.

14

Chapter 2
Effect of sowing time and N application at flowering on grain yield and
GPC of hard and soft wheat grown in volcanic ash soils in Kanto area

2.1 Introduction
To increase the self-sufficiency of wheat in Japan, the Japanese government is offeringsubsidy for wheat production in the form of “quality bonus”, which depends on its grain-quality
indices. In this system, the producers receive a special payment from the government based onfour grain-quality indices: grain protein content (GPC), falling number, ash content, and bulk
density.
Among these quality indices, GPC is the most variable, as it depends on the soil andclimatic conditions as well as the management practices. It has been widely reported that wheat
GPC is influenced by the climate, cultivar, nitrogen (N) application rate, N application timing,seeding rate, soil fertility, and the interactions among these factors (Lopez-Bellido et al., 2005;Garrido-Lestache et al., 2004; Gauer et al., 1992; Sato et al., 1992; Rao et al., 1993; Nakano
and Morita, 2009, Nakano et al., 2008).
Wheat varieties in Japan are classified into two groups, i.e., hard wheat (used for bread)and soft wheat (used for noodle), depending on the grain quality (mainly protein content)(Nakano et al. 2008) (Table 1.1). The required grain N concentrations for the hard wheat andthe soft wheat are 2.3–2.8% and 1.9–2.3%, respectively. As for GPC, the range is 11.5–14%
for hard wheat and 9.7–11.3% for soft wheat (MAFF, 2014). Wheat producers in Japan canobtain the highest quality bonus (rank A) if the GPC falls into this range.
Recent research has attested that the GPC can be controlled by N application at theflowering stage, while N application at the stem-elongation stage affects the grain yield.
However, the effectiveness of split N application at flowering for improving the GPC depends
15

on the soil type, and therefore, the control of GPC solely by split N application is difficult(Karathanasis et al., 1980; Sato et al., 1992; Nakatsuji, 2003).
The interaction between the sowing time and the effect of fertilization at flowering timeon GPC is an area that has not been duly addressed in the literatures especially in relation to
the wheat grown in volcanic ash soils.
The purpose of the study of this chapter was to test the applicability of flowering-time
N application in volcanic ash soil, in the context of late-sowing conditions, for increasing the
GPC, and to examine the possibility of introducing other varieties to stabilize the yield and
GPC, even in late-sowing situations.

16

2.2 Materials and methods

2.2.1. Experimental site
Field experiments were conducted at the Institute for Sustainable Agro-Ecosystem
Services (ISAS) (35°44′N, 139°32′E), University of Tokyo at Nishitokyo City, Tokyo. The
experimental site is located in the Kanto plain, where the volcanic ash soil, classified as Typic
Melanudand by USDA soil taxonomy, and Andosol by FAO soil classification, is dominant.
The soil layer from the surface to 30 cm depth was Kuroboku andisol (Clay loam, with bulk
density 0.73–0.78 g cm-3), and from 30 cm to 100 cm depth was Tachikawa loamy andisol(Loam, with bulk density 0.41–0.77 g cm-3) (Table 2.1). The initial soil NO3 and NH4 content
of 0-15 cm top soil layer before the fertilization in Oct. 2010 were 11.9 and 19.8 mg N kg-1,
respectively. They, however, decreased to 3.3 and 8.3 mg N kg-1 respectively in Nov. 2012
(data not shown). The NO3- and NH4+ in the soil was extracted with H2O and 2N KCl
respectively, and their concentration were measured by ion selective electrodes (Thermo Fisher
Scientific, Waltham, USA). The average annual precipitation was 1530 mm and the averageannual temperature was 16.5 °C (minimum and maximum monthly averages were 6.2 °C and27.8 °C, respectively).

17

Table 2.1Soil characteristics

Soil layer

TextureBulk density(g cm-3)

C/N*

Volumetric water content (%) at
Saturation

Top soil - Kuroboku
0–15 cm
15–30 cm
Sub soil - Tachikawa loam
30–45 cm
45–60 cm
60–80 cm
80–100 cm

Clay loam

Field
Permenantcapacity wilting point

0.73
0.78

13.7
13.1

48.2
45.7

41.9
42.0

31.6
31.8

0.77
0.45
0.41
0.45

13.8
13.8
14.5
14.5

46.4
62.4
64.5
62.1

44.7
57.7
60.2
58.3

33.4
48.7
52.4
51.6

Loam

* (Kato T, 2003)

2.2.2. Plant materials
We used four wheat varieties, Yumeshiho, Ayahikari, Nishinokaori, and Nebarigoshi;
these are the recommended varieties for the Kanto area (Yumeshiho and Ayahikari), Westernregion (Nishinokaori), and Northern region of the main island (Nebarigoshi). Yumeshiho is a
hard wheat variety with a high flour yield and good bread-making quality. Yumeshiho has ashorter culm length and is moderately resistant to leaf rust and wheat yellow mosaic virus(Kiribuchi-Otobe et al., 2009). Ayahikari is an early-maturing, high-yield soft wheat variety,which has a low amylose content that helps produce smooth noodles. It is also resistant to leaf
rust and wheat yellow mosaic virus (Yoshida et al. 2001). Nishinokaori is an early-maturing
hard wheat variety, which has a higher GPC than the other varieties in the region, but has aslightly lower yield. It has a shorter culm length and is more resistant to lodging than the othervarieties in this region (Kawase, 2009, Taya et al., 2003). Nebarigoshi is a soft wheat variety,but is also considered as a dual-purpose variety, due to its good bread-making quality (Taya etal., 2003).

18

2.2.3. Experiment 1: Effect of N application at flowering time (2010–2011 and 2011–2012)
Experiment one was conducted to see the effect of N application at flowering time inhard and soft wheat.
(1) Experimental design and treatments
The experiment comprised two cropping seasons, i.e., 2010–2011 and 2011–2012. The
experimental design was split-plot in 2010–2011 with two sowing dates (Nov 2 and Dec 1) as
the main factor, two wheat varieties (Ayahikari and Yumeshiho) as a sub factor, and four levelsof N application (0, 80, 110, and 140 kg N ha-1) as a sub-sub factor (Table 2.2). The 2011–2012 experiment used a split-plot design with the same two varieties as the main factor, andthe same N treatments as a sub-factor, with a single sowing date (Nov 16). In both the seasons,the experiment was conducted with three replications.

Table 2.2 Rate of nitrogen fertilizer application (kg N ha-1)

Year
Treatment
Experiment 1 2010–2011
N0
and
N1
2011–2012
N2
N3
Experiment 2 2012–2013

N0
N1
N2

Total N
0
80
110
140

Basal
0
60
60
60

0
80
150

0
40
80

First split(at stemSecond split
elongation) (at flowering)
0
0
20
0
20
30
20
60
0
20
40

0
20
30

(2) Crop management
N application was split into three stages: as basal, at the stem elongation stage, and atflowering (Table 2.2). Ammonium sulphate was used as the N fertilizer. The plot area was 24
m2 and 48 m2 in 2010–2011 and 2011–2012, respectively. In both years, the plots were supplied

19

with a PK fertilizer in the form of basal application at a sufficient level (P2O4 100 kg ha-1 andK2O 75 kg ha-1). Sowing was conducted using a non-till drill seeder, with 19 cm row spacing.
The sowing density and depth were 80 kg ha-1 and 25 mm, respectively. The standard sowing
window of the region is early-to-mid November.
(3) Measurements
At physiological maturity, 1 m × 0.95 m area of each plot was sampled for the analysis
of grain yield, total biomass, and harvest index. The dry matter and seed dry weight were
determined by drying the samples at 80 °C until a constant weight was achieved. The grain N
content was analysed by the dry combustion method (Elimental Analyzer Flash EA 112,
Thermo Electron Corporation, Delft), and a conversion factor of 5.7 was used to derive the
GPC from the N content. The grain yield and GPC results were corrected by adjusting the
moisture content to be 12.5% and 13.5%, respectively.

2.2.4. Experiment 2: Interaction effects of varieties, sowing dates, and fertilizer application
rates (2012–2013)
Experiment two was conducted to see the effect of the timing of sowing on grain
yield, GPC and interaction effects of varieties, sowing dates and fertilizer application(1) Experimental design and treatments
Experiment 2 comprised one field experiment conducted in the 2012–2013 cropping
season. The experimental design was split-split plot design, with four wheat varieties(Ayahikari, Yumeshiho, Nishinokaori, and Nebarigoshi) as main factor, three N levels (0, 80,
and 150 kg N ha-1 in total) as a sub factor, and four sowing dates (Oct 17, Nov 8, Nov 29, andDec 19) as a sub-sub factor.

20

(2) Crop management
N application was split into three stages: as basal, at the stem elongation stage, and atflowering. Ammonium sulphate was used as the N fertilizer (Table 2.2). The plot area was 36
m2. The variety Nebarigoshi was not sown in the Oct 17 sowing due to the unavailability of theseeds. Other crop management practices were the same as that of Experiment 1 (see 3 (2)).
(3) Measurements
At flowering, 0.5 m × 0.57 m area of each plot was sampled for measuring the total
biomass and leaf area index (LAI). The phenology was observed every week throughout theexperiment. Measurements and analysis conducted at physiological maturity were the same as
in Experiment 1. Yield components were also analysed.

2.2.5. Statistical Analysis
Experimental data for each year was subjected to analysis of variance (ANOVA) andthe means were compared with Tukey’s test at P<0.05, using R statistical analysis software (RDevelopment Core Team, 2008).

21

2.3 Results

2.3.1. Weather conditions
Figure 2.1 shows the weather conditions for the three study seasons at the experimental
site, and the average of the past 30 years (1981–2010) at the nearest weather station (Tokyo
District Meteorological Observatory, 22 km away). Considering the total precipitation during
the wheat-growing season, 2011–2012 was the wettest (1225 mm) followed by 2010–2011(1138 mm) and 2012–2013 (899 mm). The average over the last 30 years was 1157 mm. The
precipitation distribution pattern was relatively consistent among the three seasons. However,the precipitation during March and April was low in the 2010–2011 and the precipitation inearly spring (March), when the maximum temperature starts increasing rapidly, was higher in2012 compared to other years. The variation in temperature during the three growing seasons
was also similar. The minimum temperature dropped to nearly zero in December in these three
years.

22

Figure 2.1 Monthly average of daily maximum and minimum temperatures and monthly precipitation for three cropping seasons of
the experiment, and their 30-year average at the Tokyo Meteorological Station, Tokyo, Japan.

23

2.3.2. Experiment 1: Effect of N application at flowering time (2010–2011 and 2011–2012)
N application at both sowing and stem elongation stages tended to have increased
the grain yield and the total dry weight at physiological maturity (cf. N0 and N1) for both
hard and soft wheat varieties during the two seasons, although the effect was clearer for
total dry weight in hard wheat (Table 2.3). N application at flowering, however, did nothave a significant effect on the yield and total dry weight at maturity (cf. N1, N2, and N3)
except for one case (total dry weight increase at Nov 2 sowing group for Ayahikari in
2010-2011 (cf. N1 and N2)). Overall, the effect of N application was significant when all
the N treatments including N0 were compared (P<0.001), but for the grain yield and total
dry weight at maturity it was not significant when only N1–N3 were compared. The effectof sowing time × variety interaction was significant for the total dry weight (P<0.05).
When the analysis of variance was conducted excluding N0, there were significant effects
of sowing time and sowing time × variety interaction (P<0.05) in yield. There were no
remarkable differences in the harvest index across the sowing time and varieties.

24

Table 2.3 Effect of N application rate (split) and sowing time on the grain yield, GPC, total dry weight at maturity, and harvest index
in 2010–2011 and 2011–2012

Yield (g m-2)
(12.5% moisture)

Fertilizer
treatment

Total dry weight(g m-2)GPC %(13.5% moisture)

Harvest Index

Year

Sowing time

2010-2011

2-Nov

N0 0–0–0
N1 60–20–0
N2 60–20–30
N3 60–20–60

373
501
646
634

b
a
a
a

239
498
475
619

b
ab
ab
a

8.9
9.5
11.9
12.4

b
b
a
a

10.2
11.4
12.8
14.2

b
b
b
a

667
884
1082
1091

b
b
a
a

490
848
933
1077

b
a
a
a

0.49
0.50
0.52
0.51

0.42
0.51
0.44
0.51

1-Dec

N0 0–0–0
N1 60–20–0
N2 60–20–30
N3 60–20–60

194
350
417
438

a
a
a
a

250
533
474
496

b
a
a
a

12.1
13.4
13.7
14.2

b
b
b
a

11.5
13.3
14.7
15.2

b
a
a
a

318
329
677
758

a
a
a
a

437
856
798
776

b
a
a
a

0.53
0.50
0.54
0.49

0.50
0.54
0.52
0.56

16-Nov

N0 0–0–0
N1 60–20–0
N2 60–20–30
N3 60–20–60

229
447
425
489

b
a
a
a

193
408
424
445

b
a
a
a

11.7
10.7
11.2
15.6

b
b
b
a

9.4
10.0
12.8
17.1

c
c
bc
a

340
677
636
698

b
a
a
a

317
626
669
705

b
a
a
a

0.59
0.58
0.59
0.61

0.54
0.57
0.55
0.55

**
*
***
*
NS
NS
NS

(NS)
(NS)
(NS)
(*)
(NS)
(NS)
(NS)

NS
NS
NS
NS
NS
*
NS

(*)
(NS)
(NS)
(NS)
(NS)
(NS)
(NS)

2011-2012

ANOVA
NS
(*)
NS
Sowing (A)
NS
(NS)
NS
Variety (B)
***
(NS)
***
Fertilizer ( C )
NS
(*)
NS
AxB
***
(NS)
NS
AxC
*
(NS)
NS
BxC
NS
(NS)
NS
AxBxC
Fertilizer treatment denoted by same letters do not differ significantly ( P<0.05, Tukey's test)

(NS)
(*)
(***)
(NS)
(***)
(NS)
(NS)

***,**,* : significant at P < 0.001, P < 0.01 and P < 0.05 (within brackets are results excluding 0-0-0 N treatment)

25

Yumeshiho

Ayahikari

Yumeshiho

Ayahikari

Yumeshiho

Ayahikari

Ayahikari YumeshihoOn the other hand, the GPC increased significantly with increasing N application
at flowering (cf. N1, N2, and N3), but not with N application at both sowing and the stem
elongation stages (cf. N0 and N1) (Table 2.3). The only exception was the Yumeshiho
sown on Dec 1 in 2010 where GPC was increased by the N application at stem elongation
stage. Sowing time × fertilizer interaction (P<0.001) and variety × fertilizer interaction(P<0.05) were significant with respect to GPC. When the analysis of variance wasconducted excluding the zero N treatment (N0), there was a significant effect of variety
as well for the GPC (P<0.05), but the variety × fertilizer interaction was not significant.
Figure 2.2 shows that 30 kg of N application at flowering time (N2) increased the GPC
of Yumeshiho, pushing it into the supreme (rank A) quality bonus window, at both Nov
2 and Nov 16 sowing, while the GPC of Ayahikari was almost entered in this window
under both N1 and N2 at the Nov 2 and Nov 16 sowing. Supply of 60 kg N at flowering
time (N3) tended to increase the GPC beyond the quality bonus window for both varieties,
although the increase was lower for Nov 2 sowing than for Nov 16 sowing. The latesowing group (Dec 1) tended to have a higher GPC compared with other sowing group
under zero N supply (N0) for both varieties, and the N application at flowering timetended to result in an even higher GPC, above the quality bonus range, in both varieties.
Thus, the GPC content of Ayahikari and Yumeshiho sown at the optimum timing are
amenable, for reaching the quality bonus window through fertilizer application. Therefore,
application of N at the time of flowering is shown to be effective more under optimum
sowing conditions but not under late-sowing conditions.

26

2.3.3 Experiment 2: Interaction of variety, sowing date, and fertilizer application (2012–2013)(1) Phenology
As shown in Figure 2.3, both the flowering and the maturity were delayed by latesowing. In fact, the duration from sowing to maturity was shorter by 15–35 days with latesowing than with standard sowing (Nov 8). The duration from sowing to flowering was
also reduced by 16–31 days for Ayahikari and Nishinokaori, by 15-33 for Nebarigoshi
and by 14-30 for Yumeshiho under late sowing. Nebarigoshi had a longer duration fromsowing to flowering for Nov 8 and Nov 29 sowing, compared with the other threevarieties, but the duration from flowering to maturity was shorter, and thus, the maturity
dates were similar to the other varieties. For Oct 17 sowing the duration from sowing toflowering was the longest for Ayahikari followed by Yumeshiho and Nishinokaori.
However the date of maturity was more or less similar for three varieties (only Ayahikari
matured one day after other two). Nebarigoshi was not sown in Oct 17 due to seedunavailability. Duration from flowering to maturity was shorter under Dec 19 sowing for
all varieties.

27

a.

b.

Figure 2.2 GPC response of the soft wheat variety (Ayahikari) and the hard wheat
variety (Yumeshiho) to different N treatments at different sowing times (a: 2010–2011 and b: 2011–2012 experiments). Different lines represent different sowing
dates. N treatments are N0, N1, N2, and N3 (0-0-0, 60-20-0, 60-20-30, and 60-20-60
respectively). Shaded areas show the rank A quality bonus window for GPC (hardwheat: 11.5–14%; soft wheat: 9.7–11.3%).

28

●08-Nov
Ayahikari●●

19-DecSowing to Flowering Sowing toflowering to maturity maturity
186

43

229

176

40

216

▲

160

41

201

▲

145

36

181

178

38

216

▲

163

38

201

▲

145

36

181

185

43

228

175

41

216

▲

161

40

201

▲

145

36

181

182

46

228

176

40

216

▲

161

40

201

▲

145

36

181

▲

29-Nov

19-Jun18-Jun17-Jun16-Jun15-Jun14-Jun13-Jun12-Jun11-Jun9-Jun10-Jun

8-Jun

7-Jun

6-Jun

5-Jun

4-Jun

Duration ( days)▲●

17-Oct

3-Jun

2-Jun

1-Jun

13-May12-May11-May

9-May

8-May

7-May

6-May

5-May

4-May

3-May

2-May

1-May

30-Apr

29-Apr

28-Apr

27-Apr

26-Apr

25-Apr

24-Apr

23-Apr

22-Apr

21-Apr20-Apr

19-Apr

18-Apr

17-Apr

16-AprSowingdate

Date of maturity
10-MayVariety

15-AprDate of flowering

17-Oct●08-Nov
Nebarigoshi▲●

29-Nov●

19-Dec●17-Oct▲●08-Nov
Yumeshiho▲●

29-Nov●

19-Dec17-Oct08-Nov
Nishinokaori
29-Nov

●

▲

●

▲

●
●

19-DecFigure 2.3 Effect of the sowing time on the date of flowering and physiological maturity, and the duration between sowing,flowering time, and physiological maturity for different wheat varieties

29

30

Figure 2.4 Grain yield and GPC response of the soft wheat varieties (Ayahikari and
Nebarigoshi), and the hard wheat varieties (Yumeshiho and Nishinokaori) to different N
treatments at different sowing times (2012–2013 experiment). Different lines represent
different N application rates (0-0-0, 40-20-20, and 80-40-30). Shaded areas show the rank
A quality bonus window for GPC (hard wheat: 11.5–14%; soft wheat: 9.7–11.3%). No
data for Nebarigoshi in the Oct 17 sowing group.

31

Table 2.4 Effect of N application rate (split), variety, and sowing time on the dry matterproduction at flowering and maturity, and the LAI in 2012–2013
Dry matteratflowering(g m-2)
Sowing17-Oct8-Nov
29-Nov
19-DecVariety
Ayahikari
Yumeshiho
Nebarigoshi
Nishinokaori
Fertilizer
0/0/0
40/20/20
80/40/30
ANOVA
Sowing (A)Variety (B)Fertilizer ( C )
AxB
AxC
BxC
AxBxC

Dry matterat maturity(g m-2)

LAI

860.3
1062.5
678.6
740.9

b
a
c
bc

821.8
869.2
517.4
557.7

a
a
b
b

2.29
2.31
1.27
0.88

a
a
b
b

674.7
754.7
915.1
1010.8

b
b
a
a

639.5
630.2
762.1
719.3

a
a
a
a

1.43
1.51
1.64
2

a
a
a
a

726.6
871.3
903.3

b
a
a

594.6
684.7
769.2

b
ab
a

1.35
1.76
1.84

b
a
a

***
***
***
***
NA
NA
NA

**
***
***
***
NA
NA
NA

**
***
***
***
**
NA
NA

Values are expressed on a dry matter basis
Same letters do not differ significantly ( P<0.05, Tukey's test)***,**,* : significant at P < 0.001, P < 0.01 and P < 0.05

32

Table 2.5 Effect of N application rate (split), variety, and sowing time on the grain yield,
GPC, and yield components in 2012–2013

Grain yield(g m-2)
Sowing17-Oct8-Nov
29-Nov
19-DecVariety
Ayahikari
Nebarigoshi
Nishinokaori
Yumeshiho
Fertilizer
0/0/0
40/20/20
80/40/30
ANOVA
Sowing (A)Variety (B)Fertilizer ( C )
AxB
AxC
BxC
AxBxC

GPC
(%)

HI

One
thousandgrain
weight (g)Numberof grains(head-1)

Number ofheads (m-2 )

363.4
503.4
337.7
369.9

b
a
b
b

14.8
12.9
15.1
15.5

a
b
a
a

0.46
0.58
0.66
0.67

c
b
a
a

457.8
465.1
363.8
394.5

a
a
b
b

20.8
31.6
32.2
29

b
a
a
a

38
34.7
33.1
33.2

a
b
b
b

369.6
488.1
388.1
359.6

b
a
b
b

14.7
12.7
15.1
15.4

a
b
a
a

0.61
0.65
0.54
0.61

a
a
b
a

351.2
500.6
449.1
391.2

b
a
ab
b

31.8
32.5
24.1
28.3

a
a
b
ab

36.8
30.8
36.9
32.7

a
c
a
b

324.6
398.6
463.6

c
b
a

13.2
14.7
15.9

c
b
a

0.58
0.61
0.62

a
a
a

377.6
433.4
442.5

b
ab
a

28.1
27.3
31.4

a
a
a

34.2
34.8
34.7

a
a
a

**
***
***
***
**
NS
NS

**
***
***
***
***
*
*

**
***
*
NS
NS
NS
NS

***
***
NS
***
NS
NS
NS

Values are expressed on dry matter basis
Same letters do not differ significantly ( P<0.05, Tukey's test)***,**,* : significant at P < 0.001, P < 0.01 and P < 0.05

33

**
***
***
***
NS
NS
NS

***
**
**
NS
NS
NS
NS

(2) Dry matter production
The statistical analyses indicated that the effects of sowing time, variety, and N
application were significant for the total dry weight at flowering and maturity, as well as theleaf area index (LAI) at flowering (Table 2.4). These growth indices were highest for Nov 8
sowing, slightly tended to be lower for early sowing (Oct 17), and were significantly reduced
for late sowings. Late sowing particularly affected the LAI. The sowing × variety interaction
had a significant effect on all the growth indices (P<0.001), but the effect of sowing × fertilizer
interaction was significant only for LAI (P<0.01) (Table 2.4).
(3) Grain yield and yield components
Application of 80 and 150 kg N ha-1 split at sowing, stem elongation, and flowering
increased both the grain yield and the GPC significantly (P<0.001) (Table 2.5).
As for the yield components, the decreased grain yield under last sowing correlated
well with the reduction in number of heads (Table 2.5). The harvest index was rather high forlate sowing. With respect to the cultivars, the harvest index was lower for Nishinokaori (breadwheat cultivar) than for the other three cultivars. Again, the higher yield of Nebarigoshi andNishinokaori was well reflected in the number of heads per area. The significant effect ofincreased N fertilizer rate on grain yield was highly correlated with the number of heads per
area. The harvest index was also slightly increased by the N fertilizer application, although theeffect was not significant.
Figure 2.4 shows the effect of both sowing time and N application on the grain yield
and GPC. There was a general tendency that the yield was highest under the optimum sowing
conditions (Nov 8) but more or less declined in the late sowing conditions. The pattern was,however, different between Ayahikai-Yumeshiho group and Nebarigoshi-Nishinokaori group.
Yield decline from optimum sowing to late sowing tended to be less for the latter group. The

34

highest grain yield was observed for Yumeshiho (707 g m-2) at Nov 8 sowing under the highest
N application.
(4) Grain protein content (GPC)
In average the GPC was the lowest at optimum sowing (Nov 8) and significantly higher
for both later and earlier sowing (Table 2.5). Nishinokaori and Yumeshiho, the hard wheat
varieties, had a higher GPC than a soft variety Nebarigoshi, but GPC of another soft variety
Ayahikari was not different from hard varieties in this experiment. The N fertilizer application
also increased GPC significantly. The sowing time × variety and sowing time × fertilizer
interactions were also highly significant (P<0.001).
As shown in Figure 2.4, we observed a tendency of increasing GPC under delayed
sowing conditions in general. Figure 2.4 suggests that the GPC content of Ayahikari andYumeshiho sown at optimum timing are amenable to be changed, to fit into the quality bonus
window through fertilizer application, but not under late sowing conditions under which GPC
exceeded the higher limit of the quality bonus, regardless of the N application rate. Nebarigoshi
and Nishinokaori, however, was suggested to produce GPC values within the quality bonus
window even under the late-sowing conditions.

35

2.4 Discussions

2.4.1. The optimal sowing time was confirmed to be early-to-mid November
The yield results of the two experiments (Exp. 1 and 2; Tables 2.3 and 2.5, respectively)
suggested that the optimal sowing window for wheat in Kanto region, Japan, is early-to-mid
November, which is consistent with the prevalent practice in the region. Yield was significantly
lower under earlier or later sowing conditions than under this optimal sowing conditions. As
shown in Figure 2.1, the minimum temperature dropped to nearly zero in December in thesethree years, and therefore, plants sown before or during early-to-mid November could have an
opportunity to produce enough fall tillers, which resulted in a higher grain yield. The yieldcomponent analysis in Exp. 2 showed significantly higher number of heads per area under the
optimal sowing period (Table 2.5).
For the plants sown in the optimal period, better germination rates, good crop
establishment (Farroq et al., 2008), and longer vegetative period can be expected, together witha better root growth, compared with the late-sowing conditions (Barraclough & Leigh, 1984).
Thiry et al. (2002) reported that optimal-period sowing enhanced tillering during the fall, which
is important for the final grain yield. In the experiment two, number of grains per heads andone thousand grain weight significantly reduced under early sowing (Oct 17) and thereby grain
yield reduced compared to the optimal sowing (Table 2.5). Thiry et al. (2002) further reported
that, very early planting produces excessive tillering during the fall, which leads to competition
among tillers, causing a low percentage of formed spikes.

2.4.2. Delayed sowing decreased the grain yield but increased the GPC
We observed an increase in the GPC but decrease in grain yield under delayed sowing as
general pattern (Figure 2.4). It was also reported previously that late sowing increases the GPC
36

but reduces the grain yield but in soils other than the volcanic ash soils [e.g., Endoquolls andEutrocryepts soils (Subedi et al., 2007)] and reduces the one thousand grain weight (Saleem etal., 2015). Barbottin et al. (2005) reported that nitrogen remobilized at maturity was highly
positively correlated to nitrogen uptake at flowering. Therefore, when the carbohydratetranslocation is limited as a result of poor vegetative growth under late sowing, N concentrationin the grain increases due to the condensation effect. The significant interaction between thesowing time and fertilizer application rate, and between sowing time and cultivar in 2012–2013
season suggested that the effect of sowing time on grain yield and GPC is dependent on thecultivar and fertilizer application (Table 2.5).

2.4.3 The N application at flowering time to control GPC is effective only for optimal sowing
In this study, we observed the tendency that N application “at flowering” increased the
GPC, but did not increase the grain yield significantly, in both hard and soft wheat grown in
volcanic ash soils (Table 2.3). As reported in literature most grain N comes from the preanthesis assimilated N (Ehdaie & Waines., 2001), and N uptake continues during the grainfilling period (Asseng et al., 2002). Therefore, N application at flowering provides more N to
the grain. Whereas, there is no more new leaf growth in the plant after flowering time (Hay andPorter, 2006) and it is only accumulated dry matter that translocated to the grain during thegrain filling. Therefore N application at flowering may not add further benefit for increasing
grain yield. On the other hand, the N application at stem elongation tended to increase the grain
yield (Table 2.3). Stem elongation stage is important in establishing grain yield components,
and therefore, providing N during this stage is effective in increasing grain yield (Kirby, 1998;
Hay & Porter, 2006). The similar results have been reported elsewhere for the optimal-period
sowing conditions (Nakano & Morita, 2009; Woolfolk et al., 2002; Fischer et al., 1993). The
results of Table 2.3 are further explained by Figure 2.5, showing that grain yield increase was37higher than GPC increase when N applied at stem elongation whereas GPC increase was higher
than grain yield increase when further N applied at flowering time.

Figure 2.5 Grain yield-protein relationship for different N treatments; N0: 0-0-0, N1: 6020-0, N2: 60-20-30, N3: 60-20-60 (2010–2011 Nov 2 sowing and 2011–2012 experiments).
a: Yumeshiho (hard wheat), and b: Ayahikari (soft wheat). Each data point represents a
replicate.
It has been proposed that N application at flowering can be used as a management
option to adjust the GPC into the quality bonus window. My results demonstrated that theapplication of N at flowering to increase GPC was effective only for optimal-period sowing
38

but not for late sowing, because the GPC of Kanto varieties (Ayahikari and Yumeshiho) underlate sowing already exceeded the upper limit, even without N application (Figures 2.2 and 2.4).
To my knowledge, this is the first report, discussing the effectiveness of flowering time N
application in late-sowing conditions.
One of the major constraints for wheat cultivation in Kanto region, Japan, is the autumn
rain, which delays the land preparation and machine sowing using drill planter. For example,
wheat sowing was delayed in Gunma prefecture in 2016 due to the rains (Gunma Prefectural
Government, 2017). This delayed sowing causes dual economic loss to the farmers: the lower
yield and GPC surpass the quality bonus window. The present results suggest little or no N
application at the time of flowering but at the stem elongation as the reasonable N-management
strategy if the sowing is delayed.

2.4.4 Soil factors in the context of quantity of N to be applied at flowering time
According to the results, GPC of both hard and soft wheat grown in volcanic ash soils
had a tendency to surpass the quality bonus window with N application at flowering. When N
application at flowering time was increased from 30 to 60 kg N ha-1, together with high total
fertilizer application before anthesis (60 and 20 kg N ha-1 at sowing and at stem elongation,
respectively), the GPC increased up to 15.6 and 17.1% in soft and hard wheat, respectively(Table 2.3). However, according to the Nakano et al. (2008), the increase of N application at
flowering time from 30 to 60 kg N ha-1 (90 kg N ha-1 applied in total at sowing and tillering),
the GPC of hard wheat increased only to the maximum of ca. 14.5% in Gray lowland soil.
Shimazaki et al. (2015) also reported that, in volcanic ash soils, the original GPC content ishigher than in Alluvial soils. These differences in GPC is suggested to be caused by the soil
types different in the indigenous soil N supply. Eneji et al. (2002) reported that the native
nitrogen supply capacity in volcanic ash soils is higher than that in other soils, and is further
39

enhanced by the application of N fertilizers, which simulates the mineralization of the native
soil nitrogen. The indigenous N content of Gray lowland soil is, reported to be lower than thatof volcanic ash soils (Endo et al., 2013).
In 2011–2012, the GPC increased to a higher level (17.1%) compared with the otheryears (Table 2.3). The precipitation in early spring (March), when the maximum temperaturestarts increasing rapidly, was higher in 2012 compared with the other years, and it might haveenhanced the soil N supply through mineralization. These results suggested that attaining the
expected GPC in par with quality bonus standards, while increasing the grain yield, is difficult.
The general fertilizer recommendations based on the results of field experiments may not meritunless the temporal variability of indigenous soil N supply is assured.
The plant growth and grain yield were relatively higher even under no nitrogenconditions in 2010-2011 season due to the fallowing period which preceded this experiment.
The soil inorganic N content of the surface soil was in fact higher at the beginning of theexperiment with no N application but it decreased after the two years of the cultivation.

2.4.5 Efficacy of N application at flowering time is different between hard and soft wheat
varieties
The results showed that the effect of N application at flowering time was different
between hard and soft wheat varieties. The GPC content of hard wheat tended to fit into thequality bonus window, whereas that of soft wheat surpassed it, in response to the flowering
time N application (Figure 2.2). In Exp. 2 as well, it was found that 80, 40, and 30 kg N ha-1 atsowing, stem elongation, and flowering, respectively, resulted in the best N management for
the hard wheat, Yumeshiho, for attaining a high grain yield (7 t ha-1) and the standard GPC(Figure 2.4). Yumeshiho is a newly developed bread wheat cultivar, and therefore, my results
confirmed its better performance as a bread wheat cultivar in the volcanic ash soils in Kanto
40

region, Japan. On the other hand, the soft wheat (noodle) cultivar, Ayahikari, had a higher grain
yield under optimal and early sowing with the same N-management strategy; however, it
resulted in too high GPC values, surpassing the standards (Figure 2.4).

2.4.6 Adoption of cultivars from other regions that perform well under late-sowing conditions
Nebarigoshi (soft wheat) and Nishinokaori (hard wheat) are the recommended cultivars
for Tohoku and western regions in Japan, respectively. The yield of these cultivars was not ashigh as that of Yumeshiho and Ayahikari, the recommended cultivars for Kanto region, whensown at the optimum time of the year (Figure 2.4). However, these two varieties attained ahigher yield than Yumeshiho and Ayahikari, and maintained their GPCs within the quality
bonus window, under the late-sowing conditions. The higher grain yield of Nebarigoshi andNishinokaori under late sowing may be attributed to the higher number of heads per area. These
results suggest the possibility of using Nebarigoshi and Nishinokaori under late-sowing
conditions in the Kanto region, although further investigation is needed to confirm theapplicability of this strategy by evaluating the reasons for better performance of Nebarigoshi
and Nishinokaori under late sowing.

41

2.5 Conclusions
This study contributes to fill the information gap in the previous reports, regarding theinteractive effects of split N fertilizer application at flowering stage and sowing time on thegrain yield and GPC of both hard and soft wheat grown in volcanic ash soils.
The results suggested that N application at the flowering time for increasing the GPC
is more suitable under optimum sowing conditions, where the GPC content of both hard andsoft wheats can be adjusted to fit into the quality bonus window. Although early-to-mid
November is the best time for sowing wheat in Kanto region, Japan, late sowing was alsosuggested to be possible with the right choice of cultivar and a proper N management strategy.

42

Chapter 3
Effects of late sowing on yield, yield components and seed emergence at low
temperature for winter wheat in Kanto area

3.1 Introduction
The previous chapter demonstrated that the late sowing reduced the grain yield, andthereby GPC tended to increase for both hard and soft wheat of autumn sowing. The reductionof the grain yield was mainly caused by the reduction in number of heads per area. The numberof heads per area can be further divided into the number of plants per area and the number ofheads per plant. These traits can be affected by various physiological processes such as
germination, emergence and plant survival during winter period under the low temperature, but
the actual cause has not been elucidated well so far.
Therefore, a field experiment was conducted with both hard and soft wheat to conductdetailed analysis on the plant emergence and the changes of plant number during the winterperiod for the late sowing conditions in comparison to that for the optimal-time sowing
conditions.

43

3.2 Materials and Methods

3.2.1 Experimental site
A field experiment was conducted at the Institute for Sustainable Agro-Ecosystem
Services (ISAS), University of Tokyo at Nishitokyo City, Tokyo. The details of this site aredescribed elsewhere in this thesis (2.2.1). The weather data (maximum and minimum daily
temperature, precipitation and solar radiation) were obtained from the data recorded at ISAS.

3.2.2 Plant materials
Two recommended wheat varieties for Kanto region, Ayahikari and Yumeshiho wereused. See the details in Chapter 2 (2.2.2).

3.2.3 Experimental procedures
The experiment was conducted for two seasons from November 2014 to June 2016.

3.2.3.1 Experimental design and treatments
The experimental design was split-split-plot with four sowing dates as the main factor,
two wheat varieties (Ayahikari and Yumeshiho) as a subfactor, and two levels of N application(0 (N0), and 160 kg N ha-1 (N160)) as a sub-subfactor with three replications. The sowing dates
were Nov. 14, Nov. 28, Dec. 10 and Dec. 24 in 2014, and Oct. 23, Nov. 13, Dec. 4 and Dec.
22 in 2015.

44

3.2.3.2 Crop management
N application for N160 treatment was split into two stages, i.e., at basal and at the stem
elongation stage at 120 and 40 kg N ha-1, respectively, as ammonium sulphate. Each plot area
was 21 m2. Phosphorus and potassium were applied as a single compound fertilizer(0:20:15:5=N:P2O5:K2O:MgO) as basal application at a sufficient level (P2O5 100 kg ha-1 andK2O 75 kg ha-1). Sowing was conducted by a non-till drill seeder with 19 cm row spacing. The
sowing density and depth were 80 kg ha-1 and 25 mm, respectively. The standard sowing
windowisearly-to-mid

November

in

the

Kanto

region(http://www.pref.chiba.lg.jp/annou/documents/3-2-3.pdf), and therefore, October sowing is
considered as early sowing and late-November to December sowings are as late sowing.

3.2.3.3 Plant measurements
Crop phenology was recorded weekly using Zadoks scale (Zadoks et al., 1974). Seedgermination was observed one week after the each sowing for both varieties. Around 10 seeds
per plots were randomly collected by excavation with a scoop and their germination was
confirmed visually in both N0 and N160 plots of replicate two.
Plant emergence was observed every week from sowing date to till March (March
11and 29 in 2015 and 2016 respectively) and the number of emerged plants were counted
visually at the marked area of 1×0.57 m in N0 and N160 plots of replicate two. Emergence
percentage was calculated based on the initial number of plants calculated from sowing density(assumed all seed sown germinated). Date of emergence was determined as the date of 50%
emergence compared with the maximum emergence percentage by interpolating the weekly
basis emergence percentage. Maximum emergence percentage was calculated as the averageof emergence percentages after its increase ceased. At the flowering stage plants in45

0.50 × 0.57 m area of each plot was sampled for the analysis of leaf area index (LAI) and total
biomass. Subsamples were taken from the main samples and plants were separated into green
leaves, yellow and dead leaves, stems (including leaf sheath) and spikes. Leaf area wasmeasured for the green leaf samples using area meter (Li-Cor LI-3100C, Lincoln, USA). All
samples were dried at 80 °C in a forced air oven for 72 hours to obtain the dry weights.
At physiological maturity, the plants in 1 m × 0.95 m area of each plot were sampled
for the analysis of grain yield, total biomass, and harvest index. The dry matter and grain dry
weight were determined by drying the samples at 80 °C until constant weights were achieved.
The grain N content was analyzed by the dry combustion method (Elemental Analyzer FlashEA 112, Thermo Electron Corporation, Delft), and the conversion factor of 5.7 was used toderive the GPC from N content. At the sample time of physiological maturity, ten heads ofsample within each plot were collected randomly from N160 plots from each sowing groups(both varieties) to calculate the number of grains per gram stem. Calculated grains per gramstem data was used for the APSIM model calibration described in chapter 4.
Recommended herbicides were applied three weeks before the sowing and once during
the growing period. Weeds were removed manually when it is necessary. No pesticide or
fungicide was applied but the plots were relatively clean throughout the growing seasons.

3.2.3.4 Soil measurements
Soil samples were monthly collected from the plots of first sowing group of Yumeshiho
at 0-20 cm depths from March to June for two seasons. Soil samples were air-dried and passed
through 2 mm sieve. Nitrate nitrogen (NO3--N) were extracted with 20 mL of distilled waterusing 5g of air-dried soil, and their concentration was measured with a combination ionselective electrode for nitrate (9707BNWP Thermo Fisher Scientific, Waltham, USA). These46

data of soil NO3-N was used for the APSIM model validation described in Chapter 4. Soil
temperature at 5 cm depth was measured from December 2014 to February 2015 by installinga temperature sensor (ECH2O 5TE Sensor, Meter Group, Inc. USA) connected to a data logger
in the plot of 160 kg N treatment, Yumeshiho of Nov 28 sowing group, replicate two.

3.2.3.5 Analysis on the effect of temperature on plant emergence
Analysis was conducted between average air temperature (during the period from thedate of sowing to the date of emergence) and number of days taken from sowing to the date of
emergence. Average temperature from sowing to the date of emergence was calculated as a
grand average of the daily average of maximum and minimum temperature of the respective
period. Based on Milthorpe and Moorby (1979) and Addae and Pearson (1992), the relationshipbetween emergence percentage and rate of emergence (reciprocal of the number of days to thedate of emergence) was also analysed.

3.2.4 Statistical analysis
Analysis of variance was (ANOVA) was used to test the effects of year, sowing time,variety, fertilizer treatment and their interactions on grain yield, yield components, GPC, dryweight at maturity with R statistical software 3.2 (R Development Core Team 2008). To
identify the significant differences, the LSD was used when F test was significant (P<0.05).
Correlation analysis between grain yield and each yield component was conducted with thesame statistical software to elucidate the significant yield components that affected the grain
yield under late sowing conditions.

47

3.3 Results

3.3.1 Weather conditions
Both seasons for the experiment had similar patterns for temperature and rainfall.
However, October rainfall was much higher in 2014-2015 than in 2015-2016 (Figure 3.1 a).
Minimum temperature in December and February was higher in 2015-2016 than in 2014-2015.
Maximum temperature also followed the similar pattern as the minimum temperature. During
the months of October to December, daily average air temperature did not drop less than 7 ºC
in both seasons.
Daily minimum and maximum temperatures and their averages during the period from
October to March are shown in Figure 3.1 b. Daily minimum air temperature dropped below
5 ºC once in mid November but recovered again in 2014-2015 season. From December to late
February, the minimum temperature was almost consistently below 5 ºC. There were again two
spikes of lower minimum temperature in mid and late March. In 2015-2016, such period when
the daily minimum temperature was below 5 ºC continued from late November to early Marchfollowed with two colder short periods. Daily average air temperature only sporadically
dropped below 5 ºC from mid December to mid-February in 2014-2015, and from mid-January
to mid-February in 2015-2016.
The soil temperature at 5 cm depth seems to have followed the daily average airtemperature with 1-2 days delay (Figure 3.1 c). Soil temperature was consistently lower than
the air temperature from early to mid-January. There is a significant correlation between daily
average air temperature and soil temperature at 5 cm depth for these measurement (R2 = 0.77,
P< 0.001).

48

a.

b.

49

c.

DecemberJanuary

February

Figure 3.1 (a.) Monthly average of daily minimum, maximum and average temperatures
and, monthly rainfall, in 2014-2015 and 2015-2016 cropping seasons. (b.) Changes in daily
minimum, maximum and average temperatures from December to March in 2014-2015
and 2015-2016 (blue arrows denote sowing dates). (c.) Daily average air temperature andsoil temperature at 5 cm depth from 2014 December to 2015 February.

50

3.3.2 Grain yield and yield components
Results of ANOVA for yield and yield related traits showed that grain yield wassignificantly affected by sowing date, fertilizer application rate and their interaction (P<0.001
for all effects) (Table 3.1). GPC was affected by sowing date (P<0.01), variety (P<0.001),
fertilizer application rate (P<0.001), and by several interactions. Total dry weight was also
affected by sowing date, fertilizer rate, and their interaction, plus interaction between year andfertilizer rate. Regarding the yield components, number of heads per area (P<0.001), numberof grains per head (P<0.05) and one thousand grain weight (P<0.01) were significantly affectedby sowing date.
As shown in Table 3.2, grain yield was significantly reduced under latest sowing
conditions for both varieties in both seasons (except for N0 treatment in 2014-2015), comparedto that of optimal sowing dates. On the contrary, GPC increased at late sowing conditions forboth varieties in both seasons. Number of heads per area was also significantly reduced underlate sowing conditions at N160 treatment.
The degree of grain yield reduction at later sowing conditions was similar to the
reduction of number of heads per area but not to number of grains per head nor the thousand
grain weight in both hard and soft wheat varieties in two seasons (Figure 3.2). The result of thecorrelation analysis among yield and yield components showed that grain yield wassignificantly correlated with number of heads per area (r = 0.92) and with number of grains perhead (r = 0.78) but not with thousand grain weight (Table 3.3).

51

Table 3.1 Results of ANOVA for grain yield, GPC, dry matter production and yield components.

VarietyYear

Sowing time

Fertilizer
treatment

ANOVA
Year (A)Sowing (B)Variety (C)Fertilizer (D)
AxC
BxC
AxD
BxD
CxD
AxCxD
BxCxD

Yield ( g m-2 )(12.5% moisture)

GPC %( 13.5%moisture)

NS
***
NS
***
NS
NS
NS
***
NS
NS
NS

NS
**
***
***
*
NS
NS
*
*
NS
NS

Same letters do not differ significantly (P<0.05, LSD test)***,**,* : Significant at P<0.001, P<0.01, and P<0.05

52

Number of
Number of
Total dry weightgrains per
Thousandheads per area
Harvest Index
heads
grain
weight (g)( g m-2 )
(m-2)

NS
***
NS
***
NS
NS
*
***
NS
NS
NS

NS
***
**
***
NS
NS
NS
***
NS
NS
NS

NS
*
NS
***
NS
NS
NS
NS
NS
NS
NS

NS
**
***
***
NS
NS
*
*
NS
NS
NS

*
*
**
NS
*
NS
NS
*
NS
NS
NS

Table 3.2 Grain yield, GPC, dry matter production and yield components for Ayahikari (a.) and Yumeshiho (b.) in 2014-2015 and 20152016.
a.

Fertilizer
treatment-2

Yield ( g m )(12.5% moisture)

GPC %( 13.5%moisture)

Number of
Number of
Total dry weightThousandgrains perheads per area
Harvest Index-2
grain weight (g)heads( gm )
-2
(m )

Year

Sowing time

2014-201514-Nov
28-Nov
10-Dec24-DecN0 ( 0 kg N ha-1)

200
212
195
155

10.9 ab
8.7 b
10.9 ab12.5 a

290
289
278
231

182 ab
233 ab161 b
174 ab

25
23
31
23

39 a
35 b
36 b
35 b

0.61 b0.65 a0.62 ab0.59 b

14-Nov
28-Nov
10-Dec24-DecN160 ( 160 kg N ha-1)

516 a
465 a
452 a
172 b

11.7 b
12.2 b
12.9 ab14.5 a

713 a
661 a
620 a
247 b

371 a
298 ab264 ab
239 b

39 a
45 a
49 a
20 b

32
31
30
32

0.64
0.63
0.64
0.62

23-Oct13-Nov
4-Dec
22-DecN0 ( 0 kg N ha-1)

161 a
192 a
167 a
99 b

8.8 c
8.9 c
9.5 b
12.5 a

265 bc341 a
282 ab
204 c

170 b
221 a
171 b
159 b

24
21
25
18

35 b
38 a
35 b
30 c

0.54 a0.50 b0.52 ab
0.43 c23-Oct13-Nov
4-Dec
22-DecN160 ( 160 kg N ha-1)

538 a
528 a
295 b
149 c

10.9 c10.7 c12.1 b
13.6 a

998 a
873 a
501 b
250 c

375 b
418 a
290 c
137 d

37
33
31
33

35 a
34 a
30 b
29 b

0.48
0.53
0.52
0.53

2015-2016

Same letters do not differ significantly (P<0.05, LSD test)

53

b.

Fertilizer
treatment-2

Yield ( g m )(12.5% moisture)

GPC %( 13.5%moisture)

Number of
Number of
Total dry weightgrains per
Thousandheads per area
Harvest Index-2
heads
grain weight (g)( gm )
-2
(m )

Year

Sowing time

2014-201514-Nov
28-Nov
10-Dec24-DecN0 ( 0 kg N ha-1)

219
221
179
144

12.5 ab
9.5 b14.4 ab18.5 a

309
309
249
216

219
224
173
198

23
28
32
21

40 a
32 ab
31 ab
30 b

0.62
0.63
0.64
0.56

14-Nov
28-Nov
10-Dec24-DecN160 ( 160 kg N ha-1)

582 a
387 b
373 b
167 c

12.7 c12.9 c13.9 b
15.2 a

778 a
545 b
533 b
254 c

501 a
300 b
331 b
226 b

35
38
35
27

31 a
30 a
29 ab
26 b

0.66 a0.63 ab
0.62 ab0.57 b

23-Oct13-Nov
4-Dec
22-DecN0 ( 0 kg N ha-1)

120 ab184 a
176 ab111 b

9.6 b
10.2 b
10.8 ab12.1 a241 b
335 a
316 ab
239 b

189
188
192
151

17 a
25 ab
28 a
20 ab

33 ab
35 ab
30 c
31 ab

0.43 ab
0.48 ab0.49 a0.39 b

23-Oct13-Nov
4-Dec
22-DecN160 ( 160 kg N ha-1)

395 ab
463 a
316 b
146 c

11.8 ab11.7 ab11.3 b
13.2 a

819 a
822 a
539 b
296 b

386 ab
429 a
316 b
190 c

31
30
30
23

31
32
29
30

0.43
0.50
0.52
0.43

2015-2016

Same letters do not differ significantly (P<0.05, LSD test)

54

Figure 3.2 Grain yield and yield components under N160 treatment for Ayahikari (a-d)
and Yumeshiho (e-f) in 2014-2015 and 2015-2016. S1, S2, S3 and S4 are Nov-14, Nov-28,
Dec-10 and Dec-24 for 2014-2015 season and Oct-23, Nov-13, Dec-4 and Dec-22 for 20152016 season, respectively.

55

Table 3.3 Correlation coefficient among grain yield and yield components. The data of all
N treatments for two varieties in two seasons were used for the analysis.

Grain Yield
Grain Yield

Number ofgrainsper head

No of heads
per area

Thouasnd
grain weight1

No of heads per area

0.92***

1

Number of grains per head

0.78***

0.53

1

Thouasnd grain weight

-0.08

-0.15

-0.27

1

***,**,* : Significant at P<0.001, P<0.01, and P<0.05

3.3.3 Plant emergence
The changes of the emergence percentage in 2014-2015 and 2015-2016 seasons areshown in Figures 3.3 and 3.4, respectively. The results indicated that number of days takenfrom sowing to emergence was more under late sowing conditions. In both cropping seasons,plants sown under optimum sowing period reached maximum emergence percentage in around
two weeks whereas the plants sown under latest dates took almost two months to emerge. The
maximum emergence percentage tended to have decreased under late sowing conditions
compared to that under optimal sowing conditions (i.e., Nov 14 in 2014 and Nov 13 in 2015)
for both hard and soft wheat varieties.
In general the air and soil temperatures decrease under the late sowing conditions for
winter wheat sown in autumn. Therefore, as shown in Figure 3.5, the number of days toemergence increased when the average daily air temperature averaged during emergence period
decreased. It seems that the number of the days from sowing to emergence started to increase
when the average temperature decreased lower than 10 °C (Figure 3.5).

56

Figure 3.3 Changes of emergence percentage for Ayahikari (a-d) and Yumeshiho (e-h) in
2014-2015. Different sowing times denoted as; a and e: Nov-14, b and f: Nov-28, c and g:
Dec-10, d and h: Dec-24. Red data points show the sowing dates.

57

Figure 3.4 Changes of emergence percentage for Ayahikari (i-l) and Yumeshiho (m-p)
varieties in 2015-2016. Different sowing times denoted as; i and m: Oct-23, j and n: Nov13, k and o: Dec-04, l and p: Dec-22. Red data points show the sowing dates.

58

Figure 3.5 Relationship between number of days to emergence and average temperatureduring the emergence period (TAVG; ºC) for Ayahikari and Yumeshiho varieties, in
2014-2015 and 2015-2016.

3.3.3.1 Germination, the number of plants both at maximum emergence and in March
According to the field observation of seed germination, germination per se generally
completed within one week after the sowing for both varieties in all sowing dates including thelate sowing conditions. The low soil/air temperature of the late sowing did not delay thegermination ratio nor decrease the ultimate germination percentage. And therefore, number ofplants per area (m-2) at germination was assumed to be 206 and 249 in 2014, and 226 and 247
in 2015 for Yumeshiho and Ayahikari, respectively, which was calculated from the recorded
amount of seeds dropped to the field during sowing operation and thousand grain weight of theseeds measured before the sowing. Figure 3.6 shows the expected plant number at germination
calculated as above, observed plant number at maximum emergence and observed plant
number in March, for latest sowing treatment in both years. The results showed that plant
number was greatly reduced during the period between the completion of germination and at59

maximum emergence. The reduction was by 76 and 83 % for Dec. 24 sowing in 2014 and 82
and 71% for Dec. 22 sowing in 2015 for Ayahikari and Yumeshiho, respectively.
On the contrary, the decrease in number of plants during the period between maximum
emergence and in March (which corresponded to the stem elongation stage for early sownplants but to the late tillering stage for late sown plants) was minimal. The reduction was 7 and
8% for Ayahikari in 2014 and 2015 respectively, and 8% for Yumeshiho in 2015. The numberof plants, however, increased by 29% during this period for Yumeshiho for 2014 sowing.

Figure 3.6 Expected number of plants at germination, the observed number of plants at
maximum emergence and in March (m-2) for Ayahikari and Yumeshiho (Dec 24 and Dec
22 latest sowing groups of 2014-15 and 2015-16 respectively).

60

3.3.3.2 Relationship between the number of days to emergence and the number of plants at
maximum emergence
Figure 3.7 shows the relationship between maximum emergence percentage and therate of emergence (reciplocal of the number of days from sowing to the date of emergence). A
quadratic curve could fit relatively well with a high coeficient of determination (R2 = 0.8296).
The slopes (P<0.001, P<0.05) and intercept (P<0.05) were also significant. With this equation,
the maximum percentage of emergence can be calculated from the number of days needed fromsowing to emergence, for both Ayahikari and Yumeshioho varieties across the conditions of
two seasons.

Figure 3.7 Relationship between maximum emergence percentage and rate of emergence(1/ number of days to the date of emergence). ***,*: significant at P<0.001, P<0.05

61

3.4 Discussion
The weather conditions of both seasons were the typical ones of the inland area of Kanto
plane. The monthly precipitation was relatively higher in October or November due to thesporadic typhoon visits, and then it was relatively dry from December to February with around
50 mm of monthly precipitation. The precipitation increased slightly in March and April due
to so-called “spring rain” of the region. And then it rained more in June and July due to theEast Asian rainy season. The total precipitation from October to July was average (1148 mm)
in 2014-2015 but less (887 mm) in 2015-2016, compared with that of the average of past 30
years (1981-2010) at Fuchu Meteorological Station (8 km away from the experimental site)(1116 mm). Although there was some fluctuation during the entire wheat growing period, theprecipitation is not considered as the growth-limiting factor for these years.
The soil temperature at 5 cm depth should be close to the one which the sown seeds
experienced. This soil temperature, in general, followed the daily average air temperature with
1-2 days delay and there was a good correlation between daily average air temperature and soil
temperature at 5 cm depth (R2 = 0.77, P< 0.001), although it did not increase as high as the airtemperature when the latter increased (Figure 3.1 c). Since it is much easier to obtain the dataof air temperature than the soil temperature in general, and therefore air temperature was usedin this thesis to analyse the effect of low temperature on the germination and emergence of thesown seeds. This compromise would be accepted because the analytical results of the effect of
low temperature on the wheat growth is meant to be used for the model analysis where the airtemperature is the direct input data.
The results of the experiment for two seasons confirmed one of the main conclusionsof Chapter 2 that the late sowing conditions reduced grain yield and increased GPC for both
hard and soft wheat. Total dry weight, number of heads per area, number of grains per heads
and, thousand grain weight also reduced under the late sowing conditions (Table 3.2). However,62

the main yield components which affected the grain yield was found to be the number of heads
per area and number of grains per head (Table 3.3), and the former more explains the reductionin the late sowing conditions than the latter (Figure 3.2). Kelley (2001) and Tomar et al. (2014)
also reported that number of heads per area was significantly reduced for wheat grown underlate sowing conditions.
The number of heads per area can be further divided into the number of plants per area,
and the number of heads per plant, i.e., degree of tillering. The field observation in March
confirmed the lower degree of tillering in the late sowing treatment. But, since those plants
were still in the tillering stage in March and the number of tillers were increasing while theplants in early sowing treatment were already in the stem elongation stage without further
increase in the tiller number, it was assumed that the number of heads per plants was not somuch different between late and early sowing treatments. However reduction in the number ofplant population was evident from the data of maximum (ultimate) number of emergence(Figure 3.3 and 3.4). The aerial photo taken in March also showed that the poor plant standunder December sowing conditions compared to optimum sowing in Nov 14 (Figure 3.8).
Spink et al. (2000) also stated that plant population was significantly reduced under late sowing
conditions and grain yield was significantly affected by reduced plant population.
Then at which stage the number of plants per area were reduced for the late sowing
conditions? There were three possibilities as below:
1. At germination
2. From germination to emergence
3. Reduction in the number of emerged plants during winter season (winter kill)
The germination was observed one week after the sowing. Although the number ofobserved seeds was relatively limited, all the seeds had germinated by that time without

63

exception even in the late sowing treatments. Addae and Pearson (1992) reported that thegermination of wheat seed was around 96% at 5 °C and 72% at 2 °C indicating that thegermination process of wheat is relatively tolerant to cold temperature of these ranges. Basedon the observed weather conditions, the daily minimum temperature decreased to below 5 °C
from early December in 2014 and late November in 2015 (Figure 3.1c). Daily average airtemperature, however, only sporadically dropped below 5 ºC from mid-December to midFebruary in 2014-2015, and from mid-January to mid-February in 2015-2016, suggesting thatthe seeds may have experienced the low temperature at which the germination can be affected
only partially and thus it was assumed that the germination process was not affected even in
the latest sowing conditions of late December.

Figure 3.8 Aerial view of plant stand of the 2014-2015 experiment under each sowing
group (photo was taken in 2015 March 25th of March 2015).

64

The reduction in the number of emerged plants during winter season was negligible
compared to the plant number reduction due to poor emergence (Figure 3.6). The lethal lowtemperature of wheat was reported as −17.2±1.2°C (Porter & Gawith, 1999). In order to protect
critical cell structures and physiological processes during the periods of freezing temperatures,
winter cereals including wheat have a mechanism named low temperature acclimation, or
winter hardening which is regulated through complex genotypic and environmental
interactions (Fowler et al., 1996). Winter wheat needs to be able to gradually acclimatise before
a major cold event. If not, there is a possibility of subjecting to the winter kill (Alberta wheat
commission, 2013). The acclimation starts when the temperature drops below 10 °C (Fowler
et al., 1996). Therefore, winter kill is referred to when plants are not surviving under very low
temperatures owing to poor cold acclimation. It has been reported that winter wheat is best
adopted to the winter survival if it is between the three leaves to first tiller stage (Alberta wheat
commission, 2013).
During the period of the experiment in both years the average air temperature fromJanuary to March was below 10 ºC but there was no period in which the air temperature dropped
to very low temperature levels close to the lethal levels. Therefore, emerged plants may
undergo the cold acclimation in all sowing groups. However, late December sown plants
emerged in January therefore, were not in the optimal growth stage. Therefore, winter killmight be the cause of the reduction in the number of emerged plants during winter season.
Fowler (1982) has also reported that late sowing resulted in poorly established crop which weremore susceptible to winter kill. However, percentage of the number of emerged plants
reduction during the winter period was very small compared to the percentage reduction at the
emergence.
Therefore, the 2nd possibility, i.e., the plant number was reduced during the stage
between germination and emergence seems plausible. In fact Figures 3.3 and 3.4 showed that65

delayed sowing not only slowed the emergence but also decreased the ultimate emergence
percentage by around half. Addae and Pearson (1992) reported from the results of laboratory
experiment that emergence of wheat plant was not affected down to 5 ºC, but as it decreased to
2 ºC emergence was reduced to 69 – 76%.
For the purpose of this research, it was aimed at expressing the decrease of emergence
percentage due to low temperature by environmental factor(s) or other plant characteristics.
Surprisingly there were few such literatures to my knowledge, although the effect of low
temperature on the emergence rate like the results of Figure 3.5 is relatively well dealt
quantitatively (Lindstrom et al., 1976; Addae & Pearson, 1992). One of the few known
examples was the graph expressed with two lines presented by Milthorpe and Moorby (1979).
By using the field data of emergence experiment by Harper et al. (1955), they showed theultimate emergence in y-axis and rate of emergence in x-axis. The ultimate emergence wasconstant when the rate of emergence is higher than a certain threshold, but decreased linearly
below that threshold. The same approach was adopted in the present experiment (Figure 3.7).
Unfortunately the data point around the above mentioned threshold was lacking in my data,and it was difficult to approximate the relationship with two lines, and therefore a quadratic
relationship was used for the regression. The data points that were used to obtain thisrelationship comprised of 2 season’s data of 2 varieties. With the equation, the emergence
percentage can be estimated if the emergence date is known and this knowledge will be used
to reinforce the point of weakness of the wheat module of APSIM in the later chapter.
Milthorpe and Moorby (1979) also assumed that the death of the plants at lower
temperature mediated by the longer time for emergence might be related to the attack by quasiparasitic soil fungi, because the similar experiment conducted at laboratories did not have plantmortality even though time taken to emergence was increased.

66

It was observed that the emergence percentage was lower on Oct 23 (2015) earlysowing group compared to that of optimal sowing group although average temperature during
the emergence period in early sowing was relatively higher and the number of days taken to
the emergence was shorter. There was a high rainfall (45.6 mm; 33% of monthly rainfall)
between Nov 1 and Nov 8 in 2015, which coincided with seed germination period. Therefore,
reduced emergence may be due to the reduced germination percentage affected by higher
rainfall.

3.5 Conclusions
In this study, we elucidated the reasons for decreased grain yield under late sowing
conditions. Grain yield reduction by late sowing was attributed to the reduced number of heads
per area, which was mainly caused by the reduced emergence, but not by the lowered
germination rate nor the winter kill after the emergence. The lower temperature which the latesown seeds has to face not only delayed the emergence date but also the ultimate emergence
percentage. The reduction in the ultimate emergence percentage was quantitatively related tothe time required from the sowing to the date of emergence with quadratic function which will
be used for the model improvement in the later chapter of this thesis.

67

Chapter 4
Calibration and validation of APSIM crop model for standard sowing
conditions in Kanto area, Japan
4.1 Introduction
Estimating model parameters and subsequent validation of the model performances are
prerequisite for any crop growth model when the model is used to conduct simulations forcropping systems in specific soil and climatic conditions.
Parameter

estimationforsystemmodelsisreferred

to

as

model

calibration/parameterization (Wallach et al., 2014) which is being conducted to adjust
influential model parameters within their reasonable ranges so that the modeling results
become closer to observed data (Wang et al., 2013). There are three types of parameters: theenvironmental parameters such as soil properties, the crop cultivar parameters, and the
management parameters such as sowing date, fertilization, and irrigation rates (Zhao et al.,2014). Soil properties and crop cultivar parameters are used to initialize the model while
management parameters are used to modify the cultivation conditions. Soil properties for a
specific soil type can be obtained or estimated from soil test results or available soil databases.
Therefore, detailed parameter estimation is done mainly to get the crop cultivar parameters, forwhich trial-and-error method is commonly used for crop models when the specific cropparameters are not available in the published literature. Wang et al. (2013) explain in detailabout estimating the crop cultivar parameters using the trial-and-error method and Chen et al.
(2010), Mohanty et al. (2012), Balwinder-Sing et al. (2011) also used the same approach fortheir studies with APSIM crop model.
This study is the first application of APSIM crop model under Japanese condition.
Therefore, I conducted the trial-and-error approach (together with some parameters obtained

68

from experimental results) to estimate crop cultivar parameters for two Japanese wheat
cultivars grown in Kanto region, Japan.
Model validation is defined as the process of demonstrating that a given model iscapable of making sufficiently accurate predictions for specific conditions (soil, climate,cultivar, etc.). The model is said to be validated if its accuracy and predictive capability in thevalidation period have been proven to lie within acceptable limits (Refsgaard, 1997).
There are two methods to evaluate the model performance for validation: comparing
the simulated against observed values graphically and using statistical tests. The evaluation of
the model is necessary if the model is to be used for the application purposes (Soltani and
Sinclair, 2012). The experiment data other than the ones used for the parameterization should
be used for the evaluation of the model. APSIM crop growth model has been used for wheat
over a broad range of soils and climates in various parts of the world. But there has been nosuch use of APSIM model in Japanese conditions so far. Therefore, validation of APSIM model
under Japanese conditions has a significant importance when the model potential for
applications in research in the future.
This chapter describes the process of calibration and validation of APSIM model for
two Japanese wheat cultivars, i.e., Ayahikari (soft wheat) and Yumeshiho (hard wheat), grownin volcanic ash soils in Kanto region, Japan.

69

4.2 Field experiment data used for model parameterization and validation
Crop phenology, dry matter production at flowering and maturity, LAI, grain yield and
GPC data (standard sowing group) from a field experiment conducted between October 2012
and June 2013 was used for the model calibration.
Data from two field experiments conducted in 2014-2015 and 2015-2016 cropping
seasons were used for the model validation.
All field experiments were conducted at the Institute for Sustainable Agro-Ecosystem
Services (ISAS) (35º44’N, 139º32’E) of the University of Tokyo at Nishitokyo City in Tokyo.
It is located in Kanto plain where the volcanic ash soil classified as Typic Melanudand by
USDA soil taxonomy, or Andosol by FAO soil classification is dominated.
A summary of the experimental data set is indicated in Table 4.1, and detailed
experimental procedures and results of calibration data set and validation data set are explainedin Chapter 2 (2012-2013) and Chapter 3 (2014-2015 and 2015-2016).

70

Table 4.1 Summary of the field experiments used in the model calibration and validation
Year

Cultivars

Type of data usedAyahikari,
Yumeshiho

Date of flowering andmaturity,Grain yield,
GPC,
dry matterproduction at
flowering andmaturity, LAI, grain
weight,planting
density
Date of flowering andmaturity,Grain yield,
GPC,
dry matterproduction at
flowering andmaturity, LAI,
planting density, soil
water, and soil NO3Date of flowering andmaturity,Grain yield,
GPC,
dry matterproduction at
flowering andmaturity, LAI,
planting density, soil
water, and soil NO3-

2012-2013

Ayahikari,
Yumeshiho

2014-2015

Ayahikari,
Yumeshiho

2015-2016

Sowing group

N treatments

Purpose of use

Satndard(Nov. 08) ,early ( Oct. 17), andlate (Nov. 29 and Dec. 19)

0, 80, and 150 kg N ha-1

Calibration

Satndard(Nov. 14) and late (Nov. 28,
Dec. 12 and Dec. 24)

0, and 160 kg N ha-1

Validation

Satndard(Nov. 13) early ( Oct. 23), andlate (Dec. 04 and Dec. 22)0, and 160 kg N ha-1

Validation

4.3 Model Calibration and parameterization

4.3.1 Materials and methods
4.3.1.1 Model initialization

APSIM model version 7.5 was used. The model was initialized with daily weather data
(maximum temperature, minimum temperature, solar radiation, and rainfall) for the respective
cropping season. Weather data were obtained from a weather station at the experimental
location, and soil data (bulk density, saturated water content, drain upper limit, lower limit, andinitial NO3- and NH4+ concentration) obtained from soil analysis. “Fbiom” (Proportion of noninert C in microbial biomass pool) and “Finert” (Proportion of initial organic C assumed to beinert) soil parameter values were adjusted by comparing with observed dry matter production
values at the flowering stage of zero nitrogen conditions (Gaydon et al., 2012) so that APSIM
71

model can capture the original N supply capacity of the soil. No surface residue was assumed
as it was in fact the real soil surface conditions in the field at the sowing. Initial soil watercontent was set arbitrary to 70% of the available water content (between LL and DUL) filledfrom the top and the sensitivity analysis confirmed that this value does not affect the simulation
results significantly. The soil parameters (bulk density, saturated water content, drain upper
limit, lower limit, and initial NO3- and NH4+ concentration) were adjusted based on the dataobtained from soil analysis. Then the cultivar parameters/genotypic coefficients for Ayahikari,
Yumeshiho were obtained. First, four simulations were created based on sowing dates. Sowing
density, sowing depth and row spacing used for the simulation were 300 plants m-2, 25 mm and
190 mm, respectively, based on the field experiment (2012-2013). Detailed information onderiving cultivar parameters are explained under 4.3.1.2
Calibration was conducted in two steps: calibration for phenological parameters (datesof 50% flowering and physiological maturity) and growth parameters (aboveground dry matterat flowering, LAI at flowering, and aboveground dry matter at physiological maturity, grain
yield and GPC.

4.3.1.2 Deriving cultivar parameters

APSIM wheat module document explains that wheat crop takes 400 °C days to reachterminal spikelet stage from end of juvenile stage. The daily rate of accumulation of thermal
development is sensitive to photoperiod and accumulation of vernalization days. Therefore
photoperiod sensitivity and vernalization sensitivity are cultivar specific.
Therefore, we first focused on determining the photoperiod sensitivity (photop_sens(P)) and vernalization sensitivity (vern_sens (V)) for each cultivar that produces the simulated
date of flowering similar to the observed date of flowering (growth stage 68; Zadoks et al.,1974). Wang et al. (2013) also indicated that they have started deriving cultivar parameters72with photop_sens and vern_sens parameters. Before deriving photop_sens and vern_sens
parameters, the model parameter which determines the time lag before linear coleoptile growthstarts (shoot_lag units), (to adjust the date of emergence) was changed with the calculated
thermal time value. This value was calculated as the thermal time taken from sowing toemergence at optimal sowing conditions, using observed emergence data from 2014-2015 and2015-2016 experiments.
The photop_sens and vern_sens are in the ranges from one to five (APSIM wheat sourcecode file and Zhang et al., 2012). Therefore, simulation trails were run for all combinations ofthese parameters in integer with Nov. 8 sowing conditions and calculated the difference
between simulated and observed flowering date (DIF). Based on the results the range wasfurther narrowed down to seek the range that produces the lower DIF. Thereafter, simulations
were carried out for the narrowed down range for all the sowing times at 0.1 steps for bothphotop_sens and vern_sens.
Secondly, to match the simulated date of maturity with the observed date (growth stage91) the thermal time from the beginning of grain filling to maturity (tt_start_grain_fill) was
adjusted accordingly with another set of simulation trials. This parameter is the duration ofgrain filling which is cultivar specific and ranges from 500 to 800 °C days. Further, thermal
time from emergence to end of juvenile stage (tt_end_of_juvenile) parameter was adjusted tofine tune the simulated date of flowering and maturity.
After that, maximum specific leaf area for delta LAI (y_sla_max) was adjusted to
improvethe

LAI

simulation

and,maximum

grain

size

(max_grain_size)

andpotential_grain_filling_rate (grain growth rate during grain fill) parameters were adjusted
accordingly to match the simulated grain yield and observed grain yield. Grains per gram stem
(grains_per_gram_stem) parameter value was calculated from field experiment data of 2014-73

2015 experiment and calculated one thousand grain weights were used as max_grain_size forboth varieties.
This entire procedure is to derive the respective parameters for one cultivar concerned,
and therefore the same procedure was repeated for other cultivars.

4.3.2 Results of model calibration
Winter wheat crop is sown by the farmers in Kanto area, Japan in November. Therefore,
plants emerge before the winter and become dormant during the winter. Again re-growth starts
around March and reached anthesis and maturity in May and June, respectively. Therefore, thefour sowing times of the field experiment considered for model calibration consisted of early,
mid, slightly late, and late sowing periods. Variation in daily minimum and maximum
temperature and, precipitation is showed in Figure 4.1. Precipitation was relatively evenly
distributed throughout the season. Although averaged precipitation from January to March wasa little bit lower, the precipitation was higher during sowing, and vegetative growth andreproductive periods.

74

4.3.2.1 Weather data

Figure 4.1 Daily maximum and minimum temperature and rainfall averaged over amonth. Data recorded by ISAS, Nishi Tokyo, Japan (experiment location).

75

4.3.2.2 Soil data
Soil type is favorable for plant growth having higher nitrogen supplying capacity (Table
4.2) coupled with ample rainfall.

Table 4.2 Soil nitrogen and pH status at the time of sowing and Fbiom and Finert values
obtained after the adjustment to comply with zero nitrogen treatment observed values(for 0-10 and 10-40 and 40-70 soil layers)

Depth(cm)NO3(ppm)

NH4+
(ppm)

pH

Fbiom

Finert

0-10
10-40
40-70
70-100100-130

0.34
0.49
2.20
0.74
0.53

0.85
0.67
0.70
0.40
0.28

5.7
5.8
5.7
5.7
5.6

0.010
0.010
0.020
0.020
0.010

0.50
0.50
0.50
1.00
1.00

76

4.3.2.3 APSIM model parameterization
4.3.2.3.1 Parameterization for phenology
Derivation of cultivar parameters or genotype coefficients for phenology was first triedwith Nov 8 sowing data (the optimal sowing group). Then adjustments were made for othersowing times. Thus, parameters of vernalization sensitivity (vern_sens), photoperiod
sensitivity (photop_sens), thermal time from emergence to juvenile stage, thermal time fromthe beginning of grain filling to maturity were determined. The shoot_lag unit was obtained
from the filed observation. Adjusted parameter values are listed in Table 4.3.

Table 4.3 Phenological parameter values obtained for both cultivars

Phenology Parameters
Sensitivity to vernalization
Sensitivity to photoperiod
Thermal time from emergence to end of juvenile stage ( 0C d-1)
Time lag before linear coleoptile growth starts (0C d-1)
Thermal time from beginning of grain filling to maturity (0C d-1)

Default value
1.5
3
400
40
580

ValuesAyahikari
Yumeshiho
2
2
4.3
4.3
300
300
165
165
750
750

4.3.2.3.2 Parameterization for the production (grain yield, dry matter production, and leaf
area index)
The model parameterized for phenology was then run to simulate leaf area index (LAI),
dry matter production at flowering, dry matter production at maturity and grain yield. Since thesimulated results overestimated these observed values, some model parameters related yield
were adjusted with trial and error approach. The target parameters I chose were, specific leaf
area (at flowering stage), potential_grain_filling_rate, grains_per_gram_stem 1 , maximum_
grain_size. The adjusted parameters are shown in

1

Table 4.4. Out of them,

In APSIM, the number of the grains per plant is set by multiplying this parameter (grains_per_gram_stem)with the stem dry weight at flowering.

77

grains_per_gram_stem and maximum_grain_size were calculated values based on observed
field data. The other two parameters were obtained by trial-and-error method.

Table 4.4 Adjusted parameter values for each cultivar to receive better simulation resultsfor LAI, dry matter production and grain yield.

Growth Parameters
Maximum specific leaf area for delta LAI
Grains per gram stem
Maximum grain size
Potential grain filling rate

ValuesDefault value
Ayahikari Yumeshiho
27000-22000 20000-14000 22000-16000
25
61
57
0.041
0.035
0.032
0.002
0.00250.002278

4.4 Model validation

4.4.1 Materials and methods
4.4.1.1 Model initialization

As it was indicated in Table 4.1, 2014-2015, and 2015-2016 experimental data wereused for the model validation for phenology, grain yield, GPC, LAI, dry matter production at
flowering and dry matter production at maturity. Besides, observed data for soil NO3- and soil
water from 2014-2015 and 2015-2016 experiments were validated. For the validation, separate
APSIM simulations were run for each year’s sowing and nitrogen management conditions
using the calibrated model.

4.4.1.2 Methods to obtain the simulated data
Data from two field experiments conducted in 2014-2015 and 2015-2016 cropping
seasons were used for the model validation. For both Ayahikari and Yumeshiho wheat cultivars,
using the experimental procedures of two experiments, simulations were conducted to obtainthe simulated date of flowering and maturity (phenology), dry matter production, LAI, grain
yield and grain protein content with the parameterized model configuration.

4.4.1.3 Methods of model evaluation
4.4.1.3.1 Comparison of observed and simulated values
Observed and simulated phenology, grain yield, dry matter production, leaf area index(LAI) grain protein content, soil nitrate, and soil water were compared graphically to observe
the fitness of observed and model-simulated data and to see whether the model could capture
the trends of observed data.

794.4.1.3.2 Quantifying the model performances
The model performances were quantified using four statistical indices which have been
widely used in the previous reports. (e.g., Asseng et al., 1998, 2000; Wang et al., 2013; Chenet al., 2010; Zhang et al., 2012; Balwinder-Singh et al., 2011; Mohanty et al., 2012; Wu et al.,2013).
Root mean square error (RMSE), relative root mean square error (RRMSE), slope (m)of a best-fit regression line forced through the origin and modelling efficiency (ME) are thefour statistical parameters used for the model validation.
The slope (m) of the best-fitted regression line forced through the origin quantifies thepossible over or underestimation (if it is not forced through the origin the slope is not relevant
for the test; Asseng et al., 1998). The root mean square error (RMSE) gives a measure of theabsolute magnitude of the error. RMSE has the same units of measured and simulated values.
The RRMSE is a meaningful measure to compare simulation quality of data with highly
different averages (ex. yield in kg ha-1 and LAI), and it is independent of the unit used. RRMSE
is calculated by dividing the RMSE by the mean of the observed values and expressed as apercentage (Asseng et al., 1998; Wu et al., 2013; Wallach et al., 2014). The modelling
efficiency (ME) compares the deviations between predicted and observed values to thevariance of the observed values. Figure 4.1 shows the equations for calculating RMSE and ME

80

(ME)

(Source of equations: Mohanty et al., 2012)

Figure 4.2 The equations for calculating RMSE and ME

RMSE closer to 0 denotes the best model performance (lower the RMSE value betterthe performance), ME = 1 denotes perfect match of predicted and observed values and ME = 0
indicates that the model predictions are as accurate as the mean of the observed data, whereas
an efficiency less than zero (- < ME < 0) occurs when the observed mean is a better predictor
than the model. ME values between 0 and 1 are generally viewed as acceptable levels of
performance and model performance can be evaluated as satisfactory if ME > 0.5, good if ME> 0.65 and, very good if ME > 0.75 (Moriasi et al., 2007).
A variant of the RMSE is the RRMSE. Lower the RRMSE is, lesser the residual
variance (residual variance measures how accurately the model's predictions match with actual
values) is. Model accuracy was considered very good if RRMSE <10%, good if
10% < RRMSE < 20%, fair if 20% < RRMSE < 30% and poor if RRMSE > 30%, (Jamieson
et al., 1991, Magaia et al., 2017).
Model performance was tested across all sowings for phenology. But grain yield, GPC,
dry matter production and LAI were tested for optimum sowing conditions first and then for
all sowing groups (including early and late sowing conditions).

81

4.4.2 Results (Model validation)
4.4.2.1 Validation for phenology
Model-predicted and observed date of flowering and maturity from 2014-2015 and2015-2016 experiments fitted well across early, standard and late sowing conditions (Figure4.3) with RMSE ranged from 1.5 to 4.9 days for flowering time and from 0.7 to 6.5 days formaturity (Table 4.5). RMSE were higher in 2015-2016 for both cultivars (4.9 days for date offlowering and 6.5 days for date of maturity). RRMSE was lower than 6% for the date offlowering and maturity in both Ayahikari and Yumeshiho which confirm the lesser residual
variance. ME values were close to one showing the good model performance (Table 4.5).

82

Figure 4.3 Simulated and observed number of days to flowering and maturity in 20142015 and 2015-2016 for Yumeshiho (a-b) and Ayahikari (c-d). Each data point represents
different sowing group (Nov. 14, Nov. 28, Dec. 12 and, Dec. 24 in 2014-2015 and Oct. 23,
Nov. 13, Dec. 04 and Dec. 22 in 2015-2016). The continuous line is 1:1 line.

834.4.2.2 Validation results for grain yield, GPC, dry matter production and LAI (optimum
sowing)Observed grain yields varied between 170 and 468 g m-2 for Ayahikari and, between
163 and 517 g m-2 for Yumeshiho. APSIM model simulated the grain yield very well with agood agreement between observed and simulated values (Figure 4.4) for both cultivars. APSIM
model could simulate the grain yield response of Yumeshiho and Ayahikari to N application
for a wider range of N supply (from 0 to 160 kg N ha-1) (Figure 4.5). Model evaluation indices(Table 4.6) RMSE (23 g m-2 for Ayahikari and 48.2 g m-2 for Yumeshiho), RRMSE (7.2 for
Ayahikari and 15.7 for Yumeshiho), and ME (0.97 for Ayahikari and 0.88 for Yumeshiho )confirm the robustness of the model estimating the grain yield with higher accuracy.

Observed GPC varied between 10.4 and 13.5 % for Ayahikari and, between 11.8 and15.1 % for Yumeshiho. Simulation of GPC was better in Yumeshiho (RMSE 1.4%, RRMSE
10.5) than Ayahikari (RMSE 1.9 %, RRMSE 15.8). APSIM model could simulate well thevariation of observed GPC in response to the wider range applied N for both cultivars (Figure4.6). Therefore, GPC validation is acceptable for both cultivars. However, as shown in Figure
4.4 and by m values, the model tends to slightly overestimate GPC for Ayahikari (m 1.13) andunderestimate that for Yumeshiho (m 0.95).

Figure 4.4 shows the observed and predicted LAI. APSIM model could simulate theobserved LAI changes with an RMSE 0.5 for both Ayahikari and Yumeshiho. ME values(Ayhikari 0.67, Yumeshiho 0.57) (Table 4.6) showed that there is a good fitness between
simulated and observed LAI.

84

As shown in Figure 4.4, dry matter production at maturity (except for some data points)
fitted close to one to one line than dry matter production at flowering. ME also showed that
goodness of fitness better in dry matter production at maturity and acceptable for dry matterproduction at flowering (0.33 and 0.89 for dry matter production at flowering and maturity
respectively for Ayahikari and 0.76 for dry matter production at maturity for Yumeshiho).
However, dry matter production at flowering for Yumeshiho (ME -0.2) showed a tendency tooverestimate (m = 0.12) (Table 4.6).

4.4.2.3 Model performances for estimating soil nitrate concentration
The model could capture the observed soil nitrate dynamics at 0-30 cm soil layer(topsoil layer) in both years of validation (Figure 4.8). The observed range of soil NO3 was 3.940.3 g kg-1. Model error was 2.18 g kg-1 RMSE with ME 0.94 (Table 4.6).

4.4.2.4 Model performances for grain yield, GPC, dry matter production and LAI (across all
sowing conditions including early and late sowing)
After validation of the model for optimum sowing conditions, it was further tested for
across all sowing conditions in 2014-15 and 2015-16. The results showed that the model
performances went down when the data of late sowing conditions were included. The goodnessof fitness reduced and RMSE increased for all growth parameters concerned (Table 4.6 andFigure 4.7).

85

86

Figure 4.4 Simulated and observed grain yield, GPC and dry matter production at
flowering and maturity, and LAI for Ayahikari (a,b,c,d,e) and Yumeshiho ( f,g,h,i,j) in
2014-2015, and 2015-2016 at optimal sowing. The black line is 1:1 line.

87Figure 4.5 Simulated and observed grain yield in response to two different N treatments
for Ayahikari (a-b) and Yumeshiho (c-d). “a” and “c” represent the 2014-2015 data and“b," and “d” represents the 2015-2016 data.

88

Figure 4.6 Simulated and observed GPC in response to two different N treatments for
Ayahikari (a-b) and Yumeshiho (c-d). “a" and "c" represents the 2014-2015 data and"b," and "d" represents the 2015-2016 data.

89

90

Figure 4.7 Simulated and observed grain yield, GPC and dry matter production at
flowering and maturity, and LAI for Ayahikari (a,b,c,d,e) and Yumeshiho ( f,g,h, i,j) in
2014-2015, and 2015-2016 for all sowing groups. The continuous line is 1:1 line.

91

Figure 4.8 Simulated and observed soil NO3- during the 2014-2015 and 2015-2016
cropping seasons. The measurement was taken from zero N plots (a), and N applied plots(160 kg N ha-1 per season; 120 kg N ha-1 at sowing and 40 kg N ha-1 at stem elongation
stage) (b). Soil layer is 0-30 cm. Red arrows denote date of split N application at stem
elongation stage.

92

Table 4.5 Summary of APSIM model performances for Kanto area (Volcanic ash soils)
in Japan for Phenology across all sowing groups of 2014-2015 and 2015-2016

Observed
range

RMSEa

RRMSEb

2014-2015

139-167

1.8

1.21

0.96

1

2015-2016

133-180

4.9

3.1

0.92

0.99

2014-2015

175-210

1.11

0.58

0.99

0.99

2015-2016180-223

6.5

3.2

0.81

0.97

2014-2015

138-171

1.5

1.02

0.98

0.99

2015-2016

133-180

4.9

3.1

0.92

0.99

2014-2015

174-208

0.7

0.36

0.99

0.99

2015-2016180-223

6.5

3.2

0.81

0.97

Model attribute

MEc

md

Ayahikari
Number of days to flowering*Number of days to maturity*

Yumeshiho
Number of days to flowering*Number of days to maturity*a

Root mean square error
Relative root mean square errorcModel efficiencydSlope (m) of a best-fit regression line forced through the origin*Observed across four sowing groupsb

93Table 4.6 Summary of APSIM validation for Kanto area in Japan for growth parameters. The results of two different analysis werecompared (one with the data at optimum conditions only, the other with the data from all sowing groups).
Optimal sowing

Across all sowings

Model attribute

Observed
range

Observed
range

RMSEa

RRMSEbRMSEa

RRMSEbAyahikari
Grain yield (g m-2)
GPC (%)
LAI
Drymatter at flowering (g m-2)
drymatter at maturity (g m-2)

170 - 468
10.4 - 13.5
0.5 - 2.8
259 - 625
324 - 873

23
1.9
0.5
130.7
77.9

7.2
15.8
34.4
28.5
13.8

0.97
-1.1
0.67
0.33
0.89

0.96
1.13
0.82
1.18
1.1

87-478
10.1-16.8
0.3-2.8
150-749
204-997

121.5
1.9
0.6
243.3
259.3

48.6
14.6
54.7
64.8
58.6

0.2
0.14
0.4
-0.81
-0.12

1.05
1.04
0.89
1.31
1.18

Yumeshiho
Grain yield (g m-2)
GPC (%)
LAI
Drymatter at flowering (g m-2)
drymatter at maturity (g m-2)

163 - 517
11.8 - 15.1
0.5 - 2.3
283 - 777
308 - 819

48.2
1.4
0.5
221
116.9

15.7
10.5
38.1
46.1
20.8

0.88
-0.3
0.57
-0.2
0.76

0.88
0.95
1.15
1.2
1.09

99-517
11-17.9
0.4-2.4
173-777
216-821

113.1
2.5
0.9
291.6
276.1

48.6
17.4
76.7
77.6
64.9

0.11
-0.5
-0.6
-1.87
0.7

1.1
0.9
1.36
1.5
1.34

Soil NO-3 N (0-30 cm ) g kg-1

3.9 - 40.3

2.18

25.2

0.94

0.96

MEc

a

Root mean square error
Relative root mean square errorcModel efficiencydSlope (m) of a best-fit regression line forced through the origin
b

94

md

MEc

md

4.5 Discussion

4.5.1 Model calibration
The optimum sowing time, as well as the farmers' general practice for winter wheat in
Kanto area of Japan, is considered to be early to mid or mid to late November. Results of the
2012-2013 field experiment also coincided with this sowing time, as Nov 8 sowing showed thebest performance in comparison to the other three sowing times. In the Nov 8 sowing, both
hard and soft wheat cultivars showed a good response to the nitrogen fertilizer applications andachieved the highest yields, i.e., Ayahikari and Yumeshiho achieved 606.9 g m-2 and 619.4 g
m-2 respectively at the highest nitrogen application rate (150 kg N ha-1). Also, these two
cultivars are recommended for the Kanto area in Japan. Thus, we used Nov 8 sowing groupdata as the baseline for the calibration process.
First, model calibration for phenology cultivar parameters was initiated with the trialand-error method by using the field experiment results of Nov 8 sowing group data. Then they
were combined with the early and late sowing group data to obtain the phenology relatedparameters that are capable of facilitating phenology simulations over various sowing times.
Next, I derived growth-related parameters. These derived cultivar parameters were notavailable in the existing literature as this study is the first attempt to apply APSIM model inJapanese conditions. Thus, parameter values obtained in this study would be a good source for
future research work.
Of the phenology parameters concerned, “sensitivity to vernalization (vern_sens)” and“sensitivity to photoperiod (photop_sens)” are related with flowering time, and “thermal timefrom the beginning of grain filling to the maturity (tt_start_grain_fill)” is related to the date of
maturity. “Thermal time from the emergence to the juvenile period (tt_end_of_juvenile)” isrelated to early growth stage and affects the flowering date indirectly. For both cultivars, the95obtained vernalization sensitivity was two that indicates low vernalization requirement. Suchlow vernalization sensitivity also suggests that these cultivars are more spring types. KiribuchiOtobe, (2009) and Yoshida et al. (2001) also indicated that Yumeshiho and Ayahikari arespring varieties with a low degree of winter habit. After setting phenology parameters, overallsimulation capability of the model for phenology was improved across early, mid and latesowing groups of both cultivars.
Once the parameterization is done for the phenology, the model predictions werechecked for the dry matter production (at flowering and maturity), leaf area index (LAI) andgrain yield. In the initial stage, overall model predictions were overestimated, especially dry
matter productions at maturity, LAI and grain yield. Therefore, parameterization was continued
further with more growth-related parameters (Table 4.4) until model predictions develop into
reasonable values with lower error. Asseng et al. (1998, 2000) also used similar parameters to
my study in the model parameterization process. Parameter adjustments were initiated withspecific

leaf

area

values(y_sla_max)

followedbygrains

per

gram

stem

(grains_per_gram_stem), maximum grin size (max_grain_size) and, potential grain filling rate
(potential_grain_filling_rate). With this parameterization, model predictions for the standard
sowing time (Nov-8) were significantly improved. However, with the calibrated data set
APSIM model still overestimated the grain yield, dry matter production and leaf area index forlate sowing conditions (Dec 19).
On the other hand, I observed that the number of the remaining plants during the winterseason was notably lower in the late sowing treatments than in the optimum sowing timetreatment of Nov 8. As discussed in Chapter 3, it was found that the decrease of the number ofplants was mainly due to poor emergence caused by longer emerge duration owing to lowtemperature. However, in APSIM wheat model no algorithm accounts for the reduction of plant
number that occurs due to low temperature affected reduced emergence. Zhang et al. (2012)96

found that when using APSIM, the errors of phenology and yield simulations increase with thedelay in sowing date.
Detailed sensitivity analysis conducted by Zhao et al. (2014) confirmed that theparameters I used for the calibration are the most appropriate parameters due to two reasons.
Firstly, they highlighted that the yield predictions are most sensitive to the cultivar parametersthat determine the yield component (grains_per_gram_stem, max_grain_size, andpotential_grain_filling_rate) and the phenology parameters that determine length of the
reproductive stages (thermal time from floral initiation to flowing and tt_start_grain_fill).
Secondly, they showed that all ten cultivar parameters affect biomass, but amongst which theparameters of vern_sens and thermal time from floral initiation to flowering are the mostinfluential.
Japanese soil parameters are not included in APSIM soil database. Hence, one of thesoil types that have very close soil characteristics of the experiment location was selected. Thenthe soil type chosen was modified based on a field soil sample analysis to represent volcanic
ash soil type. Fbiom soil parameter values (represents the soil microbial biomass fraction) and
Finert soil parameter values (represents the organic carbon fraction which is not decomposing),
in APSIM soil parameters, were adjusted (Probert et al., 1998) to make sure that parameterizedsoil is capable enough to capture indigenous N supply capacity of volcanic ash soils, Fbiom
and Finert values were altered until the simulated indigenous N supply in the zero-N treatmentsallowed close simulation of the measured biomass data at the flowering of zero N treatment of
Nov. 8 sowing group. Gaydon et al. (2012) followed a similar method when calibrating Fbiom
and Finert using the results of field soil data analysis.

97

4.5.2 Model validation
Results of the model performance tests confirmed that APSIM model could be applied
for Japanese wheat cultivars that grow under the climatic and soil conditions in Kanto area andin other areas in Japan that has similar soil and climatic conditions. Some inter-seasonalvariations of the rainfall and temperature that were observed in the validation data set of two
cropping-seasons facilitated further meaningful validation of the model (weather data
information of validation data set is explained in Chapter 3). This validation was found to be
applicable for predicting phenology under early, optimum and late sowing conditions as well
as for predicting grain yield, GPC, dry matter production and LAI, mainly at optimum sowing
time conditions. Further model improvement that addresses the plant number reduction causedby poor emergence under late sowing conditions is needed to apply this model under latesowing conditions.
Validation results of this study for grain yield showed that RMSE of 23 g m-2 for
Ayahikari grain yield and 48.2 g m-2 for Yumeshiho grain yield. Asseng et al. (1998 and 2000),
Chen et al. (2010) and Balwinder-Sing et al. (2011) who conducted successful validation of
APSM model under standard sowing conditions for wheat in Western Australia, Netherland,
China (North China Plain) and India, reported RMSE values of 40 gm-2, 80 g m-2, 83 gm-2 and55 g m-2 respectively for grain yield.
We obtained RMSE of 77.9 g m-2 (for Ayahikari) and 116.9 g m-2 (for Yumeshiho) for
total dry matter production at maturity and RMSE of 0.5 (for both Ayahikari and Yumeshiho)
for LAI. Asseng et al. (1998 and 2000) reported RMSE of 80 g m-2 and 120 g m-2 for dry matterproduction at maturity and 0.6 and 1.2 for LAI. Furthermore, Chen et al. (2010), reported
RMSE of 140 g m-2 and 1.6 of for dry matter production and LAI respectively.
RMSE of GPC percentage validation for Ayahikari and Yumeshiho were 1.9 % and1.4 % respectively. For GPC %, Asseng et al. (1998 and 2000) obtained RMSE of 3.2 % and98

1.6 %. After introducing new grain protein routine by Asseng et al., 2002, their RMSE reduced(between 1.5% and 2 %) for temperate marine and Mediterranean regions. This proves thatgrain protein validation of my study is consistent.
Therefore, as indicated in above comparisons, my results are within the compatible
range with the existing reports on APSIM model validation. Furthermore, the results (Table
4.6) indicated higher model efficiency (ME) for both cultivars regarding phenology, grain yield,
LAI, dry matter production at maturity. Nevertheless, it showed a lower ME for the GPC
percentage (Table 4.6). However, the model performance of GPC is acceptable for two reasons,i.e., validated model could capture the GPC variation in response to the different amount of N
applied in a wider range, and the RMSE fell within in the range that was obtained by previous
studies.
Further, APSIM model could capture the soil nitrate dynamics in the topsoil layer (030 cm) in both years of validation (Figure 4.8), with a higher model efficiency (ME 0.96) fora wide range of observed values. Also, the actual change in soil nitrate in response to fertilizer
application was also represented in the model simulation. Moreover, APSIM model could
simulate the grain yield and GPC very close to that of observed under zero N conditions. These
results confirm that the model reliably incorporated the indigenous N supply of volcanic ash
soils. Thus, the validation confirmed that the parameters derived from the calibration wereappropriate and can be used effectively for prediction of wheat phenology and growthparameters (yield, GPC, dry matter production and LAI) under soil and climatic conditions in
Kanto region, Japan.
Considering all the model performance indices, comparisons between observed andsimulated yields, and response of GPC to N application and validation of soil nitrate, we havea confidence that APSIM model could be utilized successfully to support decision making in
N management as well as to aid simulation studies related to phenology, grain yield and GPC

99

of Ayahikari (soft wheat) and Yumeshiho (hard wheat) cultivars grown in volcanic ash soils of
Kanto area in Japan.
Although prediction of phenology can be applicable for optimum and late sowing
conditions, further improvements would be necessary for the prediction of yield, GPC and dry
matter production under late sowing conditions. Because, when the validation was conductedacross sowing groups, the model performance for yield, GPC and dry matter production
diminished. (Figure 4.7 and Table 4.6).
Next chapter (Chapter 5) explains the details of model improvement conducted by
combining APSIM model with an empirical model to improve the prediction accuracy of grain
yield and GPC under late sowing condition.

100

Chapter 5
Model improvement to simulate wheat growth under late sowing and thevalidation of the APSIM model across optimum and late sowings
5.1 Introduction
In the previous chapter, the results of the model validation elucidated that calibrated
APIM wheat model could simulate wheat phenology irrespective of the sowings dates. The
model, however, could not simulate the growth parameters (grain yield, GPC, dry matterproduction, and LAI) across the sowing dates because it overestimated these parameters in latesowing conditions.
Under the field conditions in Kanto region in Japan, the grain yield of autumn sown
wheat was reduced and GPC was increased when the sowing was conducted in late December(Chapters 2 and 3). Further, the careful studies on seed emergence revealed that the yield
reduction by late sowing was attributed to the reduced number of heads per area mainly causedby the reduced emergence (Chapter 3). The low temperature which the late-sown seeds had toface not only delayed the emergence but also reduced the ultimate emergence percentage.
However, the APSIM model does not have the algorithm to reduce the number of plants
when the sowing was done under the low temperature conditions (APSIM wheat model
document). The situation is same for several other major crop growth models such as Sirius,
NWheat, and STICS. CERES model has the function to deal with “winter kill” after the
emergence under extremely low temperatures (Savdie et al., 1991). But as was discussed in
Chapter 3, the wheat growing conditions in Kanto region is not so severe where only the
emergence percentage is reduced but once emerged plants could survive during the winterseason, and therefore the winter kill is not the predominant factor. Hence the approach in the
CERES model cannot be applied for this situation.

101

Therefore, in this chapter, first I aimed at introducing a new algorithm to APSIM wheat
model to reduce the plant density when the sowing and emergence should take place at low
temperature with the purpose to increase the precision of the model under the delayed sowing
conditions for winter wheat. Then, the model was revalidated for the growth parameters (grain
yield, GPC, dry matter production, and LAI) across early, optimum and late sowing conditions.

102

5.2 Materials and Methods

5.2.1 Model improvement to change plant density according to the time to emergence
It is indicated in the APSIM wheat module document that, “In the current version of
APSIM-Wheat, wheat plants are assumed to be uniclum (i.e., with a single stem), meaning thattillering is not simulated per se. While a node corresponds to a phytomer on the main stem, it
represents all the phytomers that appear simultaneously on different tillers (i.e., cohort ofleaves) in the real world” (APSIM wheat module document). The number of plants per unit
area is an input parameter in APSIM, and it is fixed throughout the simulation. After the
emergence, this number is used for simulating the total biomass and grain yield. There is noalgorithm in APSIM wheat to change the plant density when it is affected at emergence(Bangyou Zheng, personal communication, December 12, 2015).
In Chapter 3, an equation was derived to calculate the ultimate emergence percentage
from the time required from the sowing to the date of emergence. This function was added to
the “Sow on fixed date” module in the manager module of APSIM model as indicated in 5.2.1.1.

103

5.2.1.1 Modification of algorithm
The frequently used “Sow on a fixed date” module in APSIM sets the plant density andother sowing rules such as sowing depth, cultivar and row spacing by using corresponding
input parameters when the “today” is the specified sowing date (see Box 5.1).

Box 5.1 Sowing rule and new model codes<!------“Sowing rule”-------------------------------------->
if (today = date('[date]') then[crop] sow plants =[density], sowing_depth = [depth],
cultivar = [cultivar], row_spacing = [row_spacing],
crop_class = [class]endif<!------“new model codes connected to sowing rule”-------------------------------------- >if (wheat.stage = 3) then(1)

daystoemerge=DaysAftersowing(2)

x= (1/daystoemerge)(3)y= [density]*((-2357.9*x^2) + 576.6*x +12.328)/100(4)

wheat kill_stem plants= y
endif

104

(5)

Then the new 5 lines were added.
Line 1: wheat stage = 3 in APSIM indicates that the crop stage reached to “emergence”
in the do loop of the whole simulation of the time step of each day.
Line 2: a new model parameter, the number of days from sowing to emergence(daystoemerge) is defined as the number of days after sowing (DaysAftersowing) onthat day. With this new command, model calculates the number of days from sowing toemergence.
Line 3: the rate of the emergence “x” is calculated from daystoemerge.
Line 4: corrected planting density at emergence (y) is calculated from the rate of
emergence (x) and original sowing rule ([density]) by using the function derived from theregression in Figure 3.7 (Chapter 3).
Line 5: finally, “plant kill function” (wheat kill_stem) is used to adjust the plantdensity (plants) by using y.

105

5.2.2 Testing and revalidation of the improved model
5.2.2.1 Testing of the improved model
The improved model was tested with the Dec 19, 2012 late sowing data of theparameterization dataset (described in Chapter 4) for its accuracy of predicting the growthparameters under late sowing conditions for both Ayahikari and Yumeshiho. Reparameterization was conducted with trial and error simulations for Yumeshiho with a plant
related parameter “<y_leaves_per_node>” since it was still found that LAI and dry
matter production and grain yield were over estimated by the model to some extent.

5.2.2.2 Re-validation of the APSIM model after the improvement
By following the procedures described in 4.4.1.2 and 4.4.1.3, the improved model was
validated again for all sowing dates of 2014-2015 and 2015-2016 seasons for grain yield, GPC,
dry matter production at both flowering and maturity, and LAI, for both cultivars, Ayahikari
and Yumeshiho.

106

5.3 Results
It was confirmed that planting density was reduced from 300 (original planting density)
to 77 plants per m2 when the improved model was used for the Dec 19, 2012 sowing data of
the parameterization data set (2012-2013). Table 5.1 shows the new parameter settings for
Yumeshiho for the parameter – leaves per node. There was no adjustment needed for Ayahikari.

Table 5.1 Adjusted parameter values for parameter – leaves per node (only for
Yumeshiho)

Node no0
2.5
6

Default
Leaves per node
1
1
6

Yumeshiho
Leaves per node
1
0.6
0.6

After the model improvement, the fitness of simulated and observed data for grain yield,
GPC, dry matter production and LAI were much improved including late sowing data for both
Ayahikari (Figure 5.1) and Yumeshiho (Figure 5.2). Comparison of graphics before and after
the model improvement shows that data points moved towards 1:1 line.
For Ayahikari, the significantly overestimated grain yield (400-500 g m-2) of the latest
sowing date of both years reduced to around 300 g m-2 and become closer to the observed data(around 150 g m-2) (Fig 5.1, a and f). There was not much improvement in the GPC (Fig 5.1, b
and g). The significantly overestimated dry matter production at flowering (648 and 570 g m 2) and maturity ( 751 and 857 g m-2) of 2014 and 2015 were reduced (380 and 323 and, 520

and 458 g m-2 ) (Fig 5.1, c and h; d and i) to closer values of that of observed dry matterproduction which were 150 and 200 g m-2 at flowering and, around 250 g m-2 at maturity.
Observed LAI at latest sowing dates of two years were 0.9 and 0.7 in 2014 and 2015. It was107overestimated to 1.1 and 1.5 and, was reduced after the improvement to 0.5 and 0.3 (Fig 5.1, eand j) .
For Yumeshiho, the significantly overestimated grain yield (350-450 g m-2) of the latest
sowing date of both years reduced to around 250 g m-2 and become closer to the observed data(around 150 g m-2) (Fig 5.2, k and p). There was not much improvement in the GPC (Fig 5.2,
l and q). The significantly overestimated dry matter production at flowering (778 and 696 g m2

) and maturity (900 and 763 g m-2) of 2014 and 2015 were reduced (317 and 297 and, 435 and431 g m-2) (Fig 5.2, m and r; n and s) to closer values of that of observed dry matter production
which were 250 and 300 g m-2 at flowering and, between 250-300 g m-2 at maturity respectively.
Observed LAI at latest sowing dates of two years were 0.9 and 0.7 in 2014 and 2015. It wasoverestimated to 1.1 and 1.5 and, was reduced after the improvement to 0.5 and 0.3 (Fig 5.2, oand t).
The four indices for model validation showed that the overall model performance acrosssowing groups was greatly improved after the model improvement (Table 5.2). When the whole
set of sowing time data was used, RMSE for grain yield was 121.5 and 113.1 g m-2 respectively
for Ayahikari and Yumeshiho before the model improvement, but they were reduced to 79.9
and 69.0 g m-2 respectively after the model improvement. RMSE for GPC was 1.9 and 2.5%
respectively for Ayahikari and Yumeshiho, but they were reduced to 1.8 and 2.4% respectively
after the model improvement. Likewise, RMSE for the dry matter production at both floweringand maturity was also reduced. RMSE for LAI was reduced from 0.9 to 0.6 in Yumeshiho, but
not so in Ayahikari. The relative value of RMSE to average, RRMSE also followed the similar
patterns.
Modelling efficiency (ME) for grain yield improved from 0.20 to 0.65 for Ayahikari
and from 0.11 to 0.67 for Yumeshiho, both to the acceptable range. ME for GPC also improved
to a certain extent, but still, it was not acceptable, particularly in Yumeshiho. ME for LAI also108

improved but not to the extent to be accepted, particularly in Ymueshiho, again. ME for dry
matter at flowering also increased insufficiently, but that in Ayahikari was almost in theacceptable range. That at maturity increased in Ayahikari and rather decreased in Yumeshiho,
but it was anyhow acceptable for both cultivars
m value became closer to one in general due to the model improvement. As the
exceptions, m value for GPC in Ayahikari and LAI in Yumeshiho slightly more deviated fromone.
Considering the range of these model performance indices, especially RMSE and ME,
the overall model performance was acceptable for both verities.

109

110

Figure 5.1 Simulated and observed grain yield, GPC and dry matter production at
flowering and maturity, and LAI for Ayahikari, in 2014-2015, and 2015-2016 for all
sowing groups before (a,b,c,d,e) and after ( f,g,h,i,j) model improvement across all sowing
groups. The black line is 1:1 line

111

112

Figure 5.2 Simulated and observed grain yield, GPC and dry matter production at
flowering and maturity, and LAI for Yumeshiho, in 2014-2015, and 2015-2016 for all
sowing groups before (k,l,m,n,o) and after ( p,q,r,s,t) model improvement across all
sowing groups. The black line is 1:1 line

113

Table 5.2 Model performances before and after model improvement across all sowing groups (including early, optimum and late sowing)

Model attribute

Model perfromance across all sowings
before the model improvement

Model perfromance across all sowings
after the model improvement

Observed
range

RMSEa

RRMSEbRMSEa

RRMSEb87- 478
10.1-16.8
0.3-2.8
150-749
204-997

121.5
1.9
0.6
243.3
259.3

48.6
14.6
54.7
64.8
58.6

0.20
0.14
0.40
-0.81
-0.12

1.05
1.04
0.89
1.31
1.18

79.9
1.8
0.6
141.7
160.1

32.0
13.8
49.7
39.2
36.1

0.65
0.23
0.32
0.45
0.57

0.96
1.06
1.09
0.99
0.98

99-517
11-17.9
0.4-2.4
173-777
216-821

113.1
2.5
0.9
291.6
276.1

48.6
17.4
76.7
77.6
64.9

0.11
-0.50
-0.60
-1.87
0.70

1.10
0.90
1.36
1.50
1.34

69.0
2.4
0.6
146.3
131.8

29.7
17.1
57.8
38.9
31.0

0.67
-0.46
0.08
0.27
0.60

0.97
1.01
0.61
0.90
0.99

MEc

md

MEc

md

Ayahikari
Grain yield (g m-2)
GPC (%)
LAI
Drymatter at flowering (g m-2)
drymatter at maturity (g m-2)
Yumeshiho
Grain yield (g m-2)
GPC (%)
LAI
Drymatter at flowering (g m-2)
drymatter at maturity (g m-2)aRoot mean square error
Relative root mean square errorcModel efficiencydSlope (m) of a best-fit regression line forced through the originb114

5.4 Discussion

5.4.1 Validation across the sowing groups was success
It was shown in this chapter that the model improvement made the simulation output
much closer to the observed data for the entire sowing dates tested including the sowing in late
December(Figures 5.1 and 5.2). The degree of fitness of the model was evidently increased forthe case of grain yield and dry matter at maturity, and the dry matter at flowering to the lesser
extent. For GPC, although the RRMSE was relatively small, it did not further decrease owing
to the model improvement. For LAI, the RRMSE was relatively high, but it decreased by themodel improvement to a certain extent.
From these results, it can be concluded that the inclusion of the algorism of reducing
the plant density when the emergence was delayed at the late sowing conditions was successful.
And now the APSIM model can be used to for the simulation of both hard and soft winterwheat in Kanto region, and in other regions of the similar soil and climatic conditions evenunder late sowing conditions. This expansion on the applicability of the model is useful because
in Kanto region farmers from time to time face that situation that they have to sow late due toautumn rains. To the author’s knowledge, this is the first report of improving the APSIM modelby integrating an empirical model to correct the plant density at emergence due to the lowtemperature at emergence.

5.4.2 Model validation results compared with the existing literature to show the acceptance ofthe validation
After the validation across all sowing groups, RMSE for grain yield was 79.9 and 69.0
g m-2 for Ayahikari and Yumeshiho, respectively. These RMSE values are within in the rangeof RMSE for grain yield reported in the literature (40 to 85, Asseng et al., 1998; Asseng et al.,115

2000; Chen et al., 2010 and Balwinder-Sing et al., 2011). As for the GPC, RMSE for Ayahikari
and Yumeshiho were 1.8 and 2.4 % respectively which was within the range of the reported
values in other studies (1.5 – 3.2 %, Asseng et al., 1998; Asseng et al., 2000; Asseng et al.,2002).
The ME values related to the grain yield were 0.65 and 0.67 for Ayahikari andYumeshiho respectively which were within the range of that was reported by Chen et al. 2010
and Ahmed et al. 2016. (0.4-0.95). ME values between 0 and 1 are generally viewed as
acceptable levels of performance and model performance can be evaluated as satisfactory, if
ME > 0.50, good if ME > 0.65 and, very good if ME > 0.75 (Moriasi et al., 2007). Conversely,model validation was not at the level of success with the ME values of GPC as 0.23 and -0.46
for Ayahikari and Yumeshiho respectively. However, there has been no previous reports
discussing the ME value for the GPC validation of APSIM model, while RMSE was reported
to a certain extent (Asseng et al., 1998; Asseng et al., 2000; Asseng et al., 2002).
In APSIM there is no cultivar specific parameters directly related to the GPC. GPC isdetermined as the result of the balance of the inflow of carbon and nitrogen to the grains during
the grain formation period, and thus the simulation accuracy tends to be lower compared withthe dry matter production and yield. Still there should be the room of further improvement ofmodel accuracy by conducting GPC specific calibration. In fact I found that the lower ME of
GPC was caused by the overestimation at N160 treatment in Oct 23 and 13 Nov sowing groups
in 2015 for Ayahikari, and the overestimation at N160 treatment in Dec 4 sowing group in2015 for Yumeshiho. ME values was improved to 0.56 (Ayahikari) and 0.68 (Yumeshiho)
when the above mentioned data points were excluded from the calculation.

116

5.4.3 Model development approach compared with the literature
It is not only in Japan, but also farmers from some other wheat producing countries aswell have been facing the problem of reducing the wheat yields as they are compelled to sowlate due to weather and field conditions. Late planting is a major limitation to wheat
productivity in many regions of South Asia that have a rice-wheat cropping system (Hobbs etal., 1994). In Pakistan, areas with a rice-wheat cropping system, the wheat sowing is primarily
delayed because of late harvest coupled with poor soil structure and loose plant residues, which
adversely affects the preparation of the land for wheat cropping (Byerlee et al., 1984). Also,
the occurrence of rains during the land-preparation period causes a further delay in sowing inPanjab in India and Pakistan (Aslam et al., 1993, as cited in Farroq et al., 2008).
However, the model improvement to increase the accuracy under late sowing
conditions has not been done so far in APSIM (Bangyou Zheng personal communication fromthe APSIM develoment team member). The parameterization and validation of the APSIM
wheat model was conducted exclusively for the optimal sowing to the author’s knowledge(Asseng et al., 1998, 2000; Wang et al., 2013, Bai & Tao, 2017). While CERES and some othercrop growth models have the “winter kill” function, the climatic conditions where this function
was applied is different from the conditions of milder low temperature of this research.
In addition to the model improvement on planting density, I parameterized the numberof leaves per node (leaves_per_node) parameter to improve the grain yield prediction underlate sowing for the hard wheat variety, Yumeshiho. Unlike the plant density parameter (density)
defined as an input parameter, leaves_per_node is set by an x-y function in APSIM wheat
module (wheat.xml). Therefore, model improvement of this part is more complicated. And
although the observed data of number of leaves per node or plant and the tiller number per
plant regularly taken during the entire growth period is needed to derive any function to express
this parameter, but such measurement was not undertaken in the current experiments. Therefore

117additional improvement on the leaves_per_node would be the remaining research opportunity
for the future which may increase the model precision to simulate the decreased yield under
the late sowing conditions of winter wheat.

5.4.4 Recommendations for farmers
According to the observed results from Chapter 3 and simulation results in this chapter,it was shown that the low temperature affected planting density was about one-fourth of theoriginal one. Therefore, it can be suggested that the farmers would be able to compensate theyield losses under late sowing conditions by increasing the sowing density. An additional
sensitivity analysis was conducted by increasing the sowing density (by 10, 30, 50, 90, and100%) for Dec 24 latest sowing in 2014-2015 experiment. The results showed that byincreasing the sowing density by 90%, the grain yield was increased (from 252/298 to 408/415;
Yumeshiho/Ayahikari respectively) and become comparable to the one under optimal sowing
date (410/426). The simulated plant number per area also increased (from 67/56 to 128/106)
and approached to that of the optimal sowing (90/74). Studies from elsewhere had also reported
that increasing sowing density resulted in higher grain yield under late sowing conditions(Spink et al., 2000; Baloch et al., 2010; McKenzie et al., 2007 and Nazir et al., 2000). McKenzie
et al., (2007) reported that grain yield of late sown crop increased by on average 5 kg per kg
increase in seeding rate.

5.4.5 Research needs for the validation and extension of the model to adjust the emergence
percentage under low temperature conditions
In this chapter, I presented the methodology and the results for the acceptable validationand the improvement of the APSIM model for Kanto region across optimal and late sowing
conditions. The empirical equation that explains the relationship between emergence118

percentage and number of dates to the emergence was developed based only on the field
experiment of the two seasons with one soil type at single location. Therefore, it is suggested
that the similar field experiments focused on the emerged plant density and plant production
under the low temperature conditions should be conducted for other soil and climatic conditions,especially in the area and farming systems for which the late sowing is the real concern. It isneeded to assess to what extent the constants in the aforementioned equation should be
modified, or if there is need to modify the equation itself.

5.5 Conclusions
It can be concluded that, after the model improvement, APSIM model can be used forthe simulation of both hard and soft winter wheat in Kanto region or in other regions under thesimilar soil and climatic conditions under the optimal and late sowing conditions.

119Chapter 6
Scenario analysis for economically optimum nitrogen management for
Kanto region, Japan
6.1 Introduction
Japanese farmers are subsidized for wheat production through “quality bonus”
depending on the grain quality such as grain protein content (GPC). The highest quality bonus
is offered for the hard and soft wheat with the grain protein range of 11.5 - 14% and 9.7 –
11.3% (ranking A), respectively (MAFF, 2014). Therefore, farmers can obtain dual benefits ifthey attain higher grain yield with expected GPC. GPC can be controlled by N management,
but it is also affected by soil, climatic and various management factors. Therefore, decision
support for the producers is indispensable to employ best nitrogen management to attain higher
yield and required a range of GPC. The validated crop simulations models can be used for suchtasks with long-term weather data to test numerous N management scenarios to obtain optimum
N management regime.
The parameterization and validation of APSIM model was conducted for the wheat
production in Kanto area in Japan in Chapter 4, and the model improvement for the late sowing
conditions was done in Chapter 5. In this chapter, therefore, the validated model was applied
to elucidate the economic management optima for N management.
An economic analysis was conducted followed by the simulation experiment toelucidate N management optima and both benefits from yield and GPC quality bonus wereconsidered for the economic analysis.
As indicated in the chapter one (Table 1.1), there are three level of quality bonus
rankings (A, B and C). So far ranking A was considered for the evaluation of hard and soft
wheat performances (Chapter 2). Nevertheless, for the economic analysis and evaluating the N
management optima, I considered the all three levels of quality bonus rankings in this chapter.
120

6.2 Materials and methods

6.2.1 Simulation experiment for obtaining optimum nitrogen management
A simulation experiment was conducted to find out the optimum nitrogen application
rate and timing of application using the validated APSIM model configurations for both hard
wheat (Yumeshiho) and soft wheat (Ayahikari) varieties. For the weather data, daily weather
data (maximum and minimum temperature, precipitation and solar radiation) of past 55 years(1961 - 2016) was obtained at the Tokyo District Meteorological Observatory (N35°41.4',
E139°45.6')(JapanMeteorological

Agencyhttp://www.data.jma.go.jp/obd/stats/etrn/index.php).
Validated APSIM model (Version 7.5) was used to run the simulations. Sixty four N
application treatments, comprised of the full combinations of 4 levels of N rate at 3 timings,
i.e., basal, stem elongation (DC 30-31) and 50% flowering (DC 65-68) were tested. The N rates
for each timing was 0, 40, 80, and 120 kg N ha-1. The simulation experiment was conducted
for Yumeshiho (hard wheat) and Ayahikari (soft wheat) varieties, both. Growth stages aredescribed as in Zadoks decimal code scale (DC) (Zadoks et al., 1974). A pre simulation was
also conducted to identify the date of stem elongation and flowering for the fertilizer
application using Nov 14 and No 13 sowing experimental data of 2014-2016 experiments.
The simulation was run for the period from 1961/10/01 to 2016/07/28. Date of sowing
was fixed to Nov 13 and sowing density is 80 kg ha-1 (226 and 240 plants per m2 for Ayhikari
and Yumshiho, respectively) representing farmers practice in the region. Sowing depth was 2.5
cm, and row spacing was 19 cm. Soil water, Organic matter, and nitrogen were reset every year
on October 1 to ensure that they remain stable over the simulation period. Altogether 7040
simulations were conducted (55 years x 64 N scenarios x two varieties).

1216.2.2 Economic optima for the nitrogen application
Simulated grain yield and GPC were adjusted to the standard moisture contents; gain
yield to 12.5% and GPC to 13.5% moisture percentage respectively before the economic
analysis.
Economic analysis was conducted for all 64 treatments of N applications to find out thegross margin (ha-1) with respect to the respective N treatment. The calculation of the grossmargin was conducted using Excel spreadsheet based on the procedure described by Takahashi& Okada, (2013). First, the total revenue (ha-1) was calculated as a sum of the revenue received
for grain yield, quality bonus for GPC and, Acreage subsidy (universal payment). For the
calculation of quality bonus, quality bonus ranking A and C was considered for the simplicity.
If the GPC is within the range of ranking A, maximum quality bonus added or the quality bonusof rank C added otherwise (Table 6.1). However, for the evaluation of the results, all quality
bonus ranking levels (A, B and C) were taken in to the account. Detailed information on the
GPC ranges in each ranking of quality bonus and amount of quality bonus offered for hard andsoft wheat is indicated in the Table 1.1 of the chapter one.
Then, total cost was calculated as a sum of fertilizer cost, other operational cost and,capital stock payment. Table 6.1 indicate the values of each parameter used for the calculation.
Finally gross margin (ha-1) was calculated as the difference of the total revenue and the total
cost.
The gross margin was calculated from simulated grain yield and GPC for each year,
and then the average was calculated for the 55 years. The economically optimum N application
rate is the one which gives the highest averaged gross margin (among all 64 N treatments).
In addition to the 64 N treatments, an additional simulations were conducted for farmers’
N application in the region and gross margin was calculated for both varieties, respectively.

122

Table 6.1 Parameters used for calculation

Parameter

Value

SourceRevenue
Portion of wheat price unrelated to the quality (JPY t-1)
58,340 MAFF (2012)-1
Quality bonus for GPC (JPY t )Hard wheat
150,000 (11.5<p <14) or 118,833 (otherwise)
MAFF (2012)
Soft whet
107,500 (9.7<p <11.3) or 76,333 (otherwise)
Acreage subsidy (JPY ha-1)
200,000 MAFF (2012)
Cost-1
Fertilizer (JPY kg )380 Retail price (2016)-1
Other operational cost (JPY ha )377,760 MAFF (2012)-1
Capital stock payment (JPY ha )115,220 MAFF (2012)1236.2 Results
The simulated grain yield, GPC and gross margin under the different N treatment
scenarios (N1-N64) are shown in Figures 6.1 and 6.2 for Yumeshiho and Ayahikari,respectively, using box plot to visualize variation caused by the weather for 55 years. The
height of the box presents the 25-to-75 percentile, whiskers (i.e., the horizontal dashes linked
to boxes by vertical lines) present the 10-to-90 percentile, and the black line in the box is themedian.
The median grain yield of Yumeshiho increased significantly from the lowest basal N
application (0 kg N ha-1) to the highest (120 kg N ha-1) (Figure 6.1a). And the pattern of thegrain yield increase caused by the split N application was somewhat similar for each basal N
application level. Within each basal N level, grain yield increased when the N application atstem elongation increased up to 80 kg N ha-1 but very little between 80 to 120 kg N ha-1. Grain
yield response to the N application at flowering (the continuous 4 treatments within each stem
elongation N treatment) was observed only at the 0 and 40 kg N ha-1 stem elongation
application levels. At 80 and 120 kg N ha-1 stem elongation application levels, grain yield
increase was much less irrespective of the amount of N applied at flowering. In general, themore contribution to the increase of grain yield came from the basal N application followed by
the N application at stem elongation but less from N application at flowering.
The variation shown in the box plot at each N treatment is due to the year to year
variation of for 55 years, which was higher at higher grain yield levels.
The median GPC of Yumeshiho increased significantly when the N application at
flowering increased from 0 to 80 kg N ha-1 mainly within the 0 kg N ha-1 N level of stem
elongation (Figure 6.1b). In general GPC increased when the N application at stem elongation
increased from 0 to 40 kg N ha-1 and became more stable thereafter. The pattern of the GPC
increase was similar for each basal N application level.

124

The gross margin of Yumeshiho followed the same pattern as grain yield (Figure 6.1c).
At 0 and 40 kg N ha-1 N at stem elongation, gross margin increased when N application at
flowering increased. However, at 80 and 120 N ha-1 N at stem elongation, where the yield was
relatively stable, gross margin tended to decrease at higher N rate at flowering probably due tothe increased cost. Variation of gross margin was higher at higher N application levels.
For Ayahikari, the pattern of the changes of grain yield, GPC and gross margin was
similar to that of Yumeshiho in general (Figure 6.2). However the levels of highest mediangrain yield and GPC were lower than those of Yumeshiho (Figures 6.2a and c). Moreover, thevariation of GPC (height of the box) of Ayahikari at the higher N levels was slightly wider thanthat of Yumeshiho (Figure 6.2b). The highest median gross margin of Ayahikari was lower
than that of Yumeshiho even though the pattern was similar (Figure 6.2c). Similar to
Yumeshiho, gross margin increased with increasing N application at flowering only at thelower N rate at stem elongation. The variation of gross margin of Ayahikari was wider thanthat of Yumeshiho especially at 80 and 120 kg N ha-1 basal N rates.

125Figure 6.1 Variation of grain yield (a), GPC (b) and gross margin (c) of each N treatment
for Yumeshiho. The patch chart below the figure indicates treatment number (N: 1-64),
and the amount of N applied (with four colours) as basal (B), at stem elongation stage(S1), and at flowering (S2). For each box, the height of the box presents the 25-to-75
percentile, whiskers (i.e., the horizontal dashes linked to boxes by vertical lines) present
the 10-to-90 percentile, and the black line in the box is the median.

126

Figure 6.2 Variation of grain yield (a), GPC (b) and gross margin under (c) each N
treatment (N1-N64) for Ayahikari. The patch chart below the figure with four colours
shows the amount of N applied as basal (B), at stem elongation stage (S1), and at flowering(S2) under each N treatment. The boxes present the 25-to-75 percentile, whiskers (i.e., thehorizontal dashes linked to boxes by vertical lines) present the 10-to-90 percentile, theblack line in the boxes is median127

The simulated grain yield, GPC and gross margin (average of 55 years) were plottedagainst the total amount of N applied ranging from 0 to 360 kg N ha-1 (Figure 6.3) The different
data points represent each N treatment (N1-N64). Therefore, the variation within each total N
amount was due to the different proportions of N application as basal, at stem elongation andat flowering.
When the highest grain yield of Yumeshiho under each total N level was considered,
the grain yield became relatively stable at the total N rate higher than 280 kg N ha-1 (Figure 6.3
a). GPC of Yumeshiho increased up to 80 kg N ha-1of total N and became stable thereafter.
The highest average gross margin was 890,118 JPY ha-1 at N59 of 280 kg N ha-1 total N(120/80/80 for basal, at stem elongation stage and at flowering)
In case of Ayahikari also, the highest grain yield under each total N application level
was increased up to 280 kg N ha-1 of total N level and then became relatively stable (Figure6.3b). Similar to Yumeshiho, GPC of Ayahikari also increased up to 80 kg N ha-1of total N
level and became stable thereafter. The highest gross margin, 479,109 JPY ha-1 was observed
at N62 (120/120/40), which was lower than that of Yumeshiho.
Therefore, it was elucidated that the economically optimum total N application rate was
280 kg N ha-1 for both hard and soft wheat, but the proportion of split application was different
as mentioned above.

128

Figure 6.3 Variation of grain yield, GPC and gross margin with the amount of N applied.
Primary Y-axis is gross margin (10,000* JPY ha-1) secondary Y-axis is grain yield (t ha1), and GPC (%) and X-axis is the amount of N applied (0 – 360 kg ha-1). Plot “a” denotes

Yumeshiho and plot “b” denotes Ayahikari. (grain yield, GPC and, gross margin values
are average of 55 years under each N treatment (N1-N64))

129

Table 6.2 is the summary of the fertilizer application guideline for wheat in theprefectures of central and southern part of Kanto region, where the weather is similar to thesite of the present research. In Saitama and Chiba prefectures total application rate of 100 and
120 kg N ha-1 are recommended for drill sowing and broadcasting respectively. Of 120 kg N
ha-1, 80 kg N ha-1 is applied as basal and the rest is recommended to be applied in late February
to early March. No N application is recommended at flowering stage. In Ibaraki and Kanagawa
prefectures, and Tokyo Metropolis, the total fertilizer rate is from 50 to 80 kg N ha-1 which iseven lower than that of Saitama and Chiba. Based on these fertilizer guidelines, total 100 kg N
ha-1 was selected as the typical current farmers’ practice around the research site, and then,additional simulations were conducted to compare the yield and other parameters under the
elucidated most profitable N application scheme in this research and under the current farmers’
practice.

130

Table 6.2 Fertilizer application guidelines for wheat in Kanto region
Fertilizer scheme (kg/ha)
Prefecture

SowingmethodVariteyAyahikari

Soil

Land use

Total

TimingBasal Split1 of Split1

100
120
80
100
----100-120 40
Upland80100Upland100
120
Converted from paddy
80
100
Converted from paddy
100120TimingSplit2 of Split2 Ref.

Broadcast-----

Converted from paddy

20Late Feb.

0

1

Saitama Ayahikari

Drill

-----

Converted from paddy

20Late Feb.

0

1

Ayahikari-----

Alluvial

-----

Drill

-----

-----

Broadcast

-----

-----

Drill

-----

Chiba

IbarakiTokyo

Kanagawa'---- ：----Broadcast
Norin61, Kinunonami,Satonosora
Drill
Norin61, Kinunonami,Satonosora
Drill
Norin61, Kinunonami,Satonosora
Drill
Norin61, Kinunonami,Satonosora
Drill

-----

-----

-----

Black volcanic ash soil Upland

-----

-----

Alluvial

UplandNorin61

Row

Volcanic ash

UplandNorin61

Row

Alluvial

UplandNorin61

Drill

Alluvial

UplandBroadcast

Alluvial

UplandNorin61
no mention

40 Early to mid March

20-40 Heading

20

Early March

0

2

20

Early March

0

2

20

Early March

0

2

20

Early March

02Black volcanic ash soil Converted from paddy

70

60

10

Late_Feb～Early March

03

Alluvial

80

60

20

Late_Feb～Early March

03Black volcanic ash soil Upland

60

60

0

Late_Feb～Early March

03

Alluvial

80
50
60
70
65
70
70

60

20

Late_Feb～Early March

0

3

30

20

-----

0

4

40

20

-----

0

4

40

30

Mid March

0

5

45

20

Mid March

0

5

50

20

Mid March

0

5

50

20

Mid March

05

Converted from paddy

Upland

1. Guidelines for fertilizer application for major crops (March 2013), Saitama Prefecture
2. Guidelines for fertilizer application for major crops (March 2009), Chiba Prefecture
3. Guidelines for fertilizer application for major crops (March 2010), Ibaraki Prefecture
4. Guidelines for fertilizer application for major crops (unknown), Tokyo Prefecture
5. Guidelines for fertilizer application for major crops (March 2013) Kanagawa, Prefecture131According to the simulation results, the median yield at farmers’ N application scheme
was 382 and 393 g m-2 for both Ayahikari and Yumeshiho respectively (Figures 6.4a and 6.4b).
Whereas, the median grain yield at optimum N treatment was 660 and 730 g m-2 for Ayahikari
and Yumeshiho, respectively, or 72.7 and 85.7 % higher than those at the farmers’ N treatment.
Median grain yield at maximum N treatment was similar to that of optimal N scheme for bothvarieties.
Median GPC was 12.9 % in Ayahikari and 12 % in Yumeshiho at the farmers’ practice(Figures 6.4c and 6.4d). The former exceeded the ranges of both A and B quality bonus
windows of soft wheat, while the latter was within rank A quality bonus window of hard wheat.
At the optimal N treatment, GPC of Ayahikari again exceeded the both quality bonus windows,
and that of Yumeshiho was within rank B quality bonus window. Median GPC at maximum N
treatment was similar to that of the optimal N treatment in both varieties.
Median gross margin was 199,279 and 483,569 JPY ha-1 for Ayahikari and Yumeshiho
respectively, at the farmers’ N application scheme (Figures 6.4e and 6.4f). However, mediangross margins at optimum N scheme was 489,512 and 893,965 JPY ha-1 for Ayahikari andYumeshiho respectively, which is 145.6 % higher in Ayahikari and 84.8 % higher inYumeshiho compared to that at the farmers’ N application scheme. Since neither yield nor GPC
advantage increased at maximum N scheme compared to that at the optimum scheme, the grossmargin rather tended to decrease owing to the increased cost of fertilizer.

132

a

b

c

d

B (14 -15.5 %)A (11.5 -14 %)
B (11.3 -12.5 %)B (10 -11.5 %)A (9.7 -11.3 %)B (8.5 -9.7 %)

e

f

133

Figure 6.4 Variation of grain yield, GPC and, gross margin at the lowest (0_0_0), farmers’
practice (80_20_0), optimum (280; 120_120_40 / 120_80_80) and, maximum(120_120_120) N levels for Ayahikari and Yumeshiho. Plot “a,c and e” denotes Ayahikari
and plot “b,d and f” denotes Yumeshiho. Shaded areas in graph c and d denote the quality
bonus windows. Pink denotes the rank A quality bonus and green denotes rank B quality
bonus. The boxes present the 25-to-75 percentile, whiskers (i.e., the horizontal dashes
linked to boxes by vertical lines) present the 10-to-90 percentile, the black line in the boxes
is median.

134

So far the economically optimum N treatment was determined based on the grossmargin averaged for the 55 years. However, from another point of view, economically optimum
N treatment can be determined for each year by comparing the gross margin of different
treatments and therefore, optimum N treatment is expected to vary from year to year. Figure
6.5 shows such changes of optimal N treatment of each year over the 55 years, together withthe changes of grain yield, GPC and gross margin. Change in average annual temperature(AAT) and precipitation (AAP) is also plotted for comparison. During 55 years both AAT and
AAP showed an increasing trend. However the variability of the AAP among the years werevery high. The increasing trend was significant only for AAT (y = 0.0287x - 40.872, P<0.001).
The most profitable N treatment (optimum) of each year for Yumeshiho was relatively
stable and stayed slightly higher than N60 until around the year 1990 and then tended to belower (Figure 6.5a). This results was close to the most profitable N scheme (optimum)
elucidated above, i.e., N59 (120/80/80, total 280 kgNha-1). In fact, the total N rate of the mostprofitable treatment was from 240 to 280 kg N ha-1 in 31 years, more than half of the 55 yearsof the simulation. However, the N treatment number became sporadically very low for someyears (e.g. 1980, 1984) which is accompanied with the downward spikes of yield and grossmargin. The total N rate for these years were 160-200 kg N ha-1, slightly lower than that of thenormal years. Out of 55 years, GPC of was within the rank A quality bonus window for 23
years in the case of Yumeshiho.
The optimal N treatment of each year for Ayahikari also tended to stay at around N62(120/120/40, total N rate was 280 kg N ha-1) until 1990, but then started to fluctuate and tended
to be lower thereafter (Figure 6.5b). In fact, N62 was identified as the optimum N treatment
for 16 years among 55 years of the simulation period, and the total N rate was 240 to 280 kg N
ha-1 for 34 years within 55 years. Similar to the case of Yumeshiho, the grain yield and grossmargin generally followed the same pattern of the number of the N treatment. Out of 55 years,

135

GPC at the economically optimum N treatment was higher than the upper limit of rank A
quality bonus window for most of the years except only for 7 years.

136

a

b

Figure 6.5 Change in optimal N application rate over the 55 years and respective grain
yields, GPCs and gross margins of Yumeshiho (a) and Ayahikari (b). Change in annual
average temperature (AAT) and precipitation (AAP) is also indicated. Shaded area inorange is the rank A quality bonus window for both varieties. The patch chart below thefigure with four colours shows the amount of N applied as basal (B), at stem elongation
stage (S1), and at flowering (S2) under each N treatment.

137

6.3 Discussion
The purpose of this scenario analysis was to find out the economically optimum N
application scheme by testing the wider range of N treatments with different combinations ofbasal and split applications. This would provide guidance to increase profitability of the wheat
farmers in Kanto area in Japan by increasing yield and obtaining the benefits of quality bonus.
Fifty five years’ historical daily weather data was used to test the effect of long-term climate
variability on the effectiveness of each scenario.
The economically optimum N application rate derived from simulated results was 280
kg N ha-1 in total for both hard and soft wheat varieties. Although the total N amount was
similar, split N rates were different for both varieties: 120/80/80 for hard wheat, and120/120/40 for soft wheat (kg N ha-1 for basal, split at stem elongation stage and at flowering
stage). With this optimum N application scheme, farmers can obtain maximum gross marginof 890,118 JPY for hard wheat and 479,109 JPY ha-1 for soft wheat.
In the real world, the recommended fertilizer level in the mid to southern Kanto area
corresponding to the present study is from 50 to 120 kg ha-1 as the total application rate (Table
6.2). And the average wheat yield in the corresponding prefectures was 2.9, 4.0 and 3.2 t ha-1
for Ibaraki, Saitama and Chiba, respectively in 2017 (no data was available for Tokyo andKanagawa) (MAFF, 2017). If the fertilization scheme of 80/20/0 (kg N ha -1 as basal, at stem
elongation stage, and at flowering) is chosen as the typical of the region, the simulated yieldwas 3.9 and 3.8 t ha-1 for Yumeshiho and Ayahikari, respectively (the average between theyield levels of 80-0-0 and 80-40-0). These simulated yield level is, therefore, comparable tothe yield level of the statistics of the prefectures. In contrast to these fertilizer scheme of current
level, the scenario analysis suggested the possibility of attaining much higher yields and grossmargins at the higher fertilization levels with higher proportion at the later stages of the growth.

138

The lower wheat yield in Japan compared to that in typical wheat growing countries in
Europe has been the concern for many years as was discussed in the Introduction (Chapter 1.1).
Recently National Agricultural Research Organization (NARO) of Japan compared the wheat
growing conditions among those European countries, New Zealand and Japan, and found alarge difference in the N fertilizer scheme (Watanabe, 2014 and Watanabe et al., 2016). In
New Zealand and Europe generally, much lower rate of N fertilizers were applied as basal(adjusted by soil N test) and much higher rate up to 200 kg N ha-1 is applied as split application
at later stages. Then the researchers at NARO hypothesized that the general lower wheat yieldin Japan is due to the lower total N rate with higher proportion for basal application (typically
80 and 20 kg N ha-1 for basal and split application as was discusses above), and the yield canbe increased by increasing the total N rate with higher proportion at stem elongation stage. To
test this hypothesis Watanabe et al. (2016) tested higher N application rate (190 kg N ha-1 in
total) with higher dose of split application at stem elongation stage (90 kg N ha-1). The resulted
grain yield was 6 – 6.6 t ha-1, while the N rate around the conventional level (130 kg N ha-1)
resulted closer to the Japanese average level (4.3 – 4.6 t ha-1). Fujita et al. (2015) also reported
that grain yield increased up to 10 t ha-1 when the total N application rate was increased to 220290 kg N ha-1 with higher dose at later split application (80 kg N ha-1). Matsuyama et al. (2016)
reported that the application of total 270 kg N ha-1 with 3 times split application of each 80 kg
N ha-1 at later stages (stem elongation, jointing and flag leaf) for Ayahikari resulted in 7.6-7.8
t ha-1 grain yield with 13.6-13.2 % of GPC.
The optimal N rate derived from the simulation analysis of this research suggested theadvantage of increasing the rate of total N application up to 280 kg N ha-1. At the optimal N
treatment, yield of Yumeshiho and Ayahikari increased up to 7.2 and 6.6 t ha-1 respectively
which is within the range of what was reported in field experiments as discussed above. The
respective GPCs were 14.2 and 13.9 % for Yumeshiho and Ayahikari. As a negative effect of139

increasing N application rates to very high levels, Watanabe et al. (2016) reported some lodging.
The basal application of 120 kg N ha-1 recommended by current scenario analysis also might
give some risk of lodging. However, the use of 120 kg N ha-1 rate as basal application did notcause any lodging in our field experiment in 2014-2016 for both hard and soft wheat (Chapter
2).
The highest gross margin of Yumeshiho (hard wheat) obtained at optimum N scheme
was higher than that of Ayahikari (soft wheat) owing to the higher quality bonus for hard wheat
compared with that of soft wheat. Under optimum N rates, both Yumeshiho and Ayahikari
resulted in higher grain yield. However, GPC belonged to the rank B quality bonus range forhard wheat (14 – 15.5 % ) and rank C quality bonus range for soft wheat (> 12.5 %) whichgave a 5.8 % and 28 % less quality bonus compared to the premium quality bonus (rank A)
respectively for hard and soft wheat. As reported in Chapter 2, higher indigenous N supply
capacity of volcanic ash soils in Kanto region might have increased the GPC content
excessively. Therefore, the current scenario analysis of different N fertilization scheme suggestthat the producers might be able to maximize the benefit more by pursuing the higher grain
yield through higher N application, rather than suppressing the N rate trying to obtain thequality bonus by controlling the GPC. However, the strategy would be different where theindigenous N supply capacity is lower and there is a possibility to attain the target GPC bymoderately increasing the N rate compared to the current level.
The simulation results showed that yearly optimal N application scheme tend to change
to some extent due to year to year climate variation during the 55 years period in both varieties.
However during the average years it was at or closer to the optimal N scheme derived basedon the average gross margin of 55 years (280 kg N ha-1). It was indicated that yearly optimal
N scheme tended to be at lower N application rates in the years of unfavorable climate. For
example, the lowest grain yield for Ayahikari was observed in 1984 and 2004 in which140

optimum N application rate was the second lowest (160 kg N ha-1; 120/0/40) and the lowest(120 kg N ha-1 ; 80/0/40), respectively. Further, the results suggested that in unfavorable year,
it is better to target the benefits from quality bonus to maximize the gross margin rather
focusing on grain yield. This study showed that hard wheat production is more profitable thansoft wheat, because the hard wheat producers can receive benefit from both grain yield and
GPC quality bonus. Elucidated N rates were based on the economic point of views. Therefore,
it is suggested to conduct further analysis to evaluate the environmental impact of this higher
optimum N rates in order to provide further insight about the sustainability of the system.
Thus, results from the simulation study helped to evaluate the wheat farming system
for N application, and thereby providing important information to improve the productivity forbetter economy of the producers. Therefore, use of validated crop model is very useful toprovide necessary decision support to the farmers. Literature elsewhere also reported similar
studies, i.e. simulation studies for wheat cultivation with the use of APSIM crop model forevaluating wheat production systems with different perspectives. Asseng et al. (2000) derived
N management optima for wheat in Netherland using simulation analysis with validated
APSIM model. The optimal N rate they derived was 270 kg N ha-1 which resulted in 8.5 t ha-1of yield and 14 % GPC for winter wheat and there results were demonstrated to be inconsistence with the results of field experiment. Liu et al. (2016) conducted a simulation study
to encounter the soil organic C change in wheat cropping system in Australia with long-termclimate variability in relation to different N application rates. Bai et al. (2016) evaluated theeffect of modern fertilizer and other agronomic management practices to increase the yield inrice wheat rotation system in China during past 28 years mitigating effects of climate change.
Luo et al. (2009) had studied the wheat grain yield response to changes in N application rateand in cultivar choice under dry and wet climatic conditions which will be expecting under
future climate change in Australia. However, this is the first study of such simulation scenario141

analysis conducted to provide N management decision support to improve the wheat
productivity in Japan.
6.4 Conclusions
The results of this simulation study indicated that economically optimum rate of total
N application for both Yumeshiho (hard wheat) and Ayahikari (soft wheat) was 280 kg N ha-1.
Split N rates were, however, different: 120-80-80 kg N ha-1 for Yumeshiho, and 120-120-40
for Ayahikari (at sowing, at stem elongation stage and at flowering, respectively). With these
optimum N treatments, the producers can obtain by 84.8 and 145.6 % higher median grossmargin (one out of 2 year chance) compared to the current level for Yumeshiho and Ayahikari,respectively. Thus, the use of APSIM model for decision support on N management helped toelucidate optimum N application regimes for typical hard and soft wheat varieties grown in
Kanto region, Japan using a broad range of N application rates even taking the long-termclimate variation into account.

142

Chapter 7
APSIM model application to Hokkaido region: parameterization,
validation and scenario analysis for economically optimum nitrogen
management
7.1 Introduction
Hokkaido is the largest wheat producing region in Japan which accounts for 64.7% of
the total national production. Tokachi plain, Toumon-Kitami region, Furano basin, and Ishikari
plain are referred as the four wheat production centers and of which first three together named
as the eastern field region and the forth named as the western paddy region (Nihei, 2012).
The total wheat production area in Hokkaido is about 120,000 ha, of which 90%
produces the winter wheat. According to the statistics 75% of area is cultivated with a soft
wheat variety named Kitahonami which is the major wheat cultivar grown at present followedby Yumechikara, a hard wheat cultivar. Yumechikara is cultivated in 9.5 % of the total area.
Therefore, these are the two representative, leading wheat verities in the Hokkaido region atpresent.
In the previous chapter, I elucidated the N management optima for hard and soft wheat
in Kanto region with a simulation study using APSIM crop model. In this chapter, firstly, I
focused on the parameterization and validation of APSIM model for the major hard and soft
wheat cultivars of Hokkaido region. Secondly, I conducted simulation study to elucidate theeconomically optimum N management optima for the Hokkaido region.

1437.2 Materials and methods
7.2.1 Model calibration and validation
ASPIM model calibration and validation was conducted for Kitahonami andYumechikara winter wheat varieties which are the mostly grown representative soft and hard
wheat cultivars in Hokkaido.
APSIM model 7.5 was used throughout this chapter. Soil data for the Hokkaido wereobtained from the soil grid (https://www.soilgrids.org) based on the geographical coordinatesof the experimental locations (Kunneepu, Kitami and Sapporo) where the data of filed
experimental were obtained. Soil water data were estimated using the soil texture data as
described by Saxton and Rawls, (2006). Description of soil data is indicated in Table 7.2.
Daily weather data were obtained from the nearest weather station data of Japan
Meteorological Agency. For Kitami, it was Kitami weather station (17.3 Km away from
Kunneppu experimental location) and for Sapporo, it was Sapporo weather station (13 Km
away from experimental location). Since the daily solar radiation data were not available for
Sapporo, it was obtained from NASA power data (https://power.larc.nasa.gov/), based on thegeographical coordinates.
Model calibration and validation was conducted using the published field experimental
data and description about the data set is mentioned in Table 7.1. Calibration and validation
procedure was as same as described in Chapter 4.

144

Table 7.1 Description of the experimental data used for model calibration and validation
Variety

Kitahonami

Source of data

Area

Year of experiment

Kasajima et al. 2016

Kitami, Hokkaido

1. 2011-2012,
2. 2012-2013

1 40/50/0
2. 40/50/50(B/SE/FL)

Yamada et al. 2016

Kitami, Hokkaido

1. 2014-2015
2. 2015-2016

56/50(B/FL)

Araki 2016

Kitami, Hokkaido

2014-2015

Kitami, Hokkaido

2011-2012
1. Sowing denisty 140(Sep 14 and 21 sowing)
2. Sowing density 100(Sep 21 sowing)

Sawaguchi et al. 2014

Yumechikara

Fueki et al. 2015a

Kitami, Hokkaido

1.2007-2008
2.2008-2009

Tsuji et al. 2009

Sapporo, Hokkaido

2007-2008

Tsuji et al. 2010

Sapporo, Hokkaido

2008-2009

N application Use of data

Calibration

Validation:
phenology

60/20/40/40 Validation:(B/SE/PI/FL) grain yieldN1. 60/40/40/60
Validation:(B/Boot/PI/FL)
grain yieldN2. 60/80/60(B/PI/FL)

N1: 40/80/40(B/Boot/FL)
N2: 40/80/40(B/PI/FL)

40/90(B/SM)

Validation:
GPC

Calibration

N1: 40/0/0/0(B/SM/PI/FL) Validation:
N2: 40/90/0/0 Phenology(B/SM/PI/FL) Grain yieldN3: 40/90/30/0 GPC(B/SM/PI/FL)

Stage of fertilizer application: B - Basel, Boot-Booting, PI-Panicle initiation, FL-Flag leaf, SM-After snow melting

145

Table 7.2 Description of soil data
Soil physical parameters
Area

Depth (mm) Sand
(%)

Clay
(%)

Kitami(43º 45' N, 143º 43' E)

0-5
5-20
20-50
50-110110-210

0.40
0.39
0.41
0.43
0.45

0.21
0.21
0.20
0.21
0.22

Sapporo(43º 0' N, 141º 24' E)

0-5
5-20
20-50
50-110110-210

0.42
0.43
0.43
0.45
0.45

0.17
0.19
0.20
0.21
0.22

Volumetric water content (%) at
PermenentBD
Fieldwilting
Saturation-3
capacity( g cm )
point
1.20
0.13
0.27
0.42
1.30
0.13
0.27
0.42
1.40
0.13
0.26
0.41
1.50
0.13
0.26
0.40
1.50
0.13
0.26
0.40
0.70
0.70
0.70
1.40
1.50

0.11
0.12
0.12
0.13
0.13

0.24
0.24
0.25
0.25
0.25

0.40
0.39
0.39
0.39
0.40

7.2.2 Scenario analysis to derive N management optima
7.2.2.1 Simulation experiment for obtaining optimum nitrogen management
A simulation experiment was conducted to find out the optimum nitrogen application rate
and timing of application using the validated APSIM model configurations for both hard wheat(Yumechikara) and soft wheat (Kitahonami) varieties for 30 years, with past 30 years daily
weather data (1986-2016). Daily weather data was obtained from Kitami and Sapporo weather
stations of Japan Meteorological Agency, for Kitahonami and Yumechikara respectively(7.2.1). Sowing date was Sep 20 which is in optimal sowing window of Hokkaido. Sowing
rated used was 225 seed per m2. Row spacing was 200 mm and sowing depth was 25 mm. The
simulation experiment comprised of 64 different nitrogen application treatments which
comprised of a basal and two split application at stem elongation stage (DC 30-31) andflowering stage (DC 65-68).
The fertilizer application rates and all experimental procedures are as same as describedin 6.2.1 in Chapter 6. Altogether 3840 simulations were conducted (30 x 64 x 2). Grain yield,
GPC were recorded for each year under each N scenario for each variety.

146

Simulated grain yield and GPC were adjusted to the standard moisture contents; gain
yield to 12.5% and GPC to 13.5% moisture percentage respectively before the economic
analysis.

7.2.2.2 Economic optima for the nitrogen application
The gross margin was calculated from simulated grain yield and GPC for each year,
and then the average was calculated for the 30 years. The economically optimum N application
rate is the one which gives the highest averaged gross margin (among all 64 N treatments).
In addition to the 64 N treatments, an additional simulations were conducted for farmers’
N application in the region and gross margin was calculated for both varieties respectively.
Detailed procedure for calculating the gross margin is described in elsewhere of thisthesis (6.2.2, Chapter 6).

147

7.3 Results

7.3.1 Model calibration and validation
Calibration of APSIM model for Kitahonami and Yumechikara was conducted with theexperimental data from Kasajima et al. 2016 and Tsuji et al. 2009 (Table 7.1). Derived cultivar
parameters for Kitahonami and Yumechikara are shown in Table 7.3. All parameters except
maximum grin weight were derived with trial and error method. For the maximum grain weight,
1000 grain weights of two cultivars, mentioned in the experimental data were used.

Table 7.3 Cultivar parameter values obtained for both cultivarsa.
Phenology Parameters
Sensitivity to vernalization
Sensitivity to photoperiod0-1

Thermal time from emergence to end of juvenile stage ( C d )Thermal time from end of juwenile to floral initiation stage (0 C d-1)
0-1

Thermal time from beginning of grain filling to maturity ( C d )ValuesDefault value Kitahonami Yumechikara
1.5
3.4
4.9
3.0
1.0
2.3
400
465
350
555

544

555

580

390

750

b.
ValuesDefault value Kitahonami Yumechikara
27000-22000 27000-22000 22000-16000
25
33
40
0.041
0.042
0.043
0.002
0.01
0.009

Growth Parameters
Maximum specific leaf area for delta LAI
Grains per gram stem
Maximum grain size
Potential grain filling rate

*1000 grain weight used as maximum grain size

148

After obtaining the cultivar parameters, validation of APSIM model for the phenology, grain
yield and GPC of Kitahonami and Yumechikara was conducted with the experimental data.
For Kitahonami, the number of days from sowing to flowering and maturity data from Yamada
et al. (2016) was used for the validation of the phenology while the grain yield data from Araki(2016) and Sawaguchi et al. (2014) was used for the validation of grain yield. GPC data from
Fueki et al. (2015) was used for the GPC validation.
Graphical comparison of simulated and observed phenology, grain yield and GPC ofboth varieties with 1:1 line is shown in Figure 7.1. It showed that the respective data points ofnumber of days to flowering and maturity, grain yield and, GPC of both varieties were fitted
close to 1:1 line showing that simulated data are fitted with that of observed.
Observed data set used for the validation were from the field experiments conductedwith wider range of N application and different timing of application. It was between 100 and
200 kg N ha-1 for Kitahonami data set and between 40 and 160 kg N ha-1 for Yumechikara data
set. The change in grain yield and GPC of Observed data was manifested in simulated data as
well (Figure 7.2) which indicates that model could capture the response of two varieties to theamount of N applied and the timing of application. Furthermore, Sawaguchi et al. (2014) data
set was comprised of two sowing times and sowing densities. Therefore, the grain yieldvalidation of Kitahonami was included the effect of sowing densities and sowing times.

149

Figure 7.1 Simulated and the observed number of days to flowering and maturity, grain
yield and GPC of Kitahonami (a-c) and Yumechikara (d-f).

150

a

Sawaguchi et al. 2014 ( N1: 60/40/40/60 ; Basal/Booting/Panicle initiation/Flag leaf N2: 60/80/60; Basal/Panicle initiation/Flag leaf)
Araki 2016 ( 60/20/40/40; Basal/Stem elongation/Panicle initiation/Flowering)
Fueki et al. 2015 (N1:40/40/80; Basal/Flag leaf/Booting N2:40/40/80; Basal/Flag leaf/Panicle initiation)
Tsuji et al. 2010 ( N1: 40; basal N2: 40/90; Basal/After snow melting N3: 40/90/30; Basal/After snow melting/Panicle initiation)

Figure 7.2 Simulated and the observed grain yield and GPC of Kitahonami (a-b) and Yumechikara (c-d) at different N application
treatments.
151

The results of APSIM model performance for Kitahinami and Yumechikara evaluatedbased on statistical indices (RMSE, RRMSE, ME and, m) are shown in Table 7.4.
For Kitahonami, RMSE values were 6 days, 75 g m-2, and 1% for phenology, grain
yield and, GPC respectively. The respective RRMSE values were 2.1, 10.4, and 10.4% whichwere within <10 % range for phenology and within 10-20 % range for grain yield and GPC.
Modelling efficiency (ME) values for phenology, grain yield and GPC were 0.88, 0.08 and,
0.98 respectively. m values for phenology, grain yield and GPC were 1.00, 0.94, and 1.04
respectively. According to the m value, grain yield simulation shows slightly underestimation
and accordingly ME value was also lower compared to that of phenology and GPC.
For Yumechikara, RMSE values were 4 days, 86 g m-2, and 0.5 % for phenology, grain
yield and, GPC respectively. The respective RRMSE values were 1.2, 17.2, and 4.9 % whichwere within <10 % range for phenology and within 10-20 % range for grain yield and GPC.
ME values for phenology, grain yield and GPC were 0.94, 0.5 and, 0.3 respectively. m values
for phenology, grain yield and GPC were 1.00, 1.14, and 1.02 respectively. According to them value, grain yield and GPC simulation shows slightly overestimation and accordingly ME
values were also lower compared to that of phenology.

152

Table 7.4 Summary of APSIM model performances for Hokkaido region

Model attribute

Observed
range

RMSEa RRMSEb

MEc

md

Kitahonami
Phenology (days)
Grain yield (g m-2)
GPC (%)

267-309587-815
9.8-10.2

6.0
75.0
1.0

2.1
10.4
10.4

0.88
0.08
0.98

1.00
0.94
1.04

265-315327-617
10.1-11.7

4.0
86.0
0.5

1.6
17.2
4.9

0.94
0.50
0.30

1.00
1.14
1.02

Yumechikara
Phenology (days)
Grain yield (g m-2)
GPC (%)a

Root mean square error
Relative root mean square errorcModel efficiencydSlope (m) of a best-fit regression line forced through the originb153

7.3.2 Scenario analysis to derive N management optima
The simulated grain yield, GPC and gross margin under the different N treatment
scenarios (N1-N64) are shown in Figure 7.3 and 7.4 for Kitahonami and Yumechikara,respectively, using the box plot to visualize variation caused by weather for 30 years. The
height of the box presents the 25-to-75 percentile, whiskers (i.e., the horizontal dashes linked
to boxes by vertical lines) present the 10-to-90 percentile, and the black line in the box is themedian.
The median grain yield of Kitahonami increased significantly from the lowest basal N
application level (0 kg N ha-1) to the 40 kg N ha-1. From 40 to 120 it increased but the increase
was smaller than that of at 40 kg N ha-1. (Figure 7.3 a). However the pattern of the grain yield
increase was relatively same for each basal N application level. Within each basal N level (upto 80 kg N ha-1), grain yield increased when the N application at stem elongation increased up
to 80 kg N ha-1. However, increase of grain yield from 0 to 40 kg N ha-1 is higher than that of
40 to 80 kg N ha-1. Grain yield response to the N application at flowering (the continuous 4
treatments within each stem elongation N treatment) was observed only at the 0 and 40 kg N
ha-1 stem elongation application levels. At 80 and 120 kg N ha-1 stem elongation applicationlevels, grain yield increase was less irrespective of the amount of N applied at flowering. In
general, the more contribution to the increase of grain yield came from the basal N application
followed by the N application at stem elongation but less from N application at flowering.
The variation shown in the box plot at each N treatment is due to the year to year
variation of weather for 30 years, which was higher at all grain yield levels.
The median GPC of Kitahonami increased significantly when the N application at
flowering increased from 0 to 40 kg N ha-1 within the 0 kg N ha-1 N level at stem elongation(Figure 7.3 b). In general GPC increased when the N application at stem elongation increased
from 0 to 40 kg N ha-1 and became more stable thereafter. The pattern of the GPC increase was154same for each basal N application level. There was a higher year to year variation of the GPC
at all N application levels.
The gross margin of Kitahonami followed the same pattern as grain yield (Figure 7.3c). At 0 kg N ha-1 N application levels at stem elongation, gross margin increased when the N
application at flowering increased up to 40 kg N ha-1 and decreased thereafter. On the other
hand, gross margin increased with increased N application up to 40 kg N ha-1 at stem elongation
within the 0 and 40 kg N ha-1 at basal application levels. Variation of the gross margin was very
high at predominantly 80 and 120 kg N ha-1 basal application levels. This higher variation wasobserved at 50-75 percentile.
In general, the pattern of the changes of grain yield, GPC and gross margin of
Yumechikara was similar to that of Kitahonami (Figure 7.4). However the levels of highest
median grain yield and GPC were higher than those of Kitahonami. Particularly, increase ofgrain yield in response to 80 and 120 basal N application levels was higher than that of
Kitahonami. Moreover, the variation of GPC (height of the box) of Yumechikara at the higher
N levels was narrower than that of Kitahonami. The highest median gross margin of
Yumechikara was higher than that of Kitahonami and the variation (height of the box) of thegross margin was lower than that of Kitahonami at higher N levels. Similar to Kitahonami,
gross margin increased with increasing N application at stem elongation and decreased with N
application at flowering.

155

a

b

c

156

Figure 7.3 Variation of grain yield (a), GPC (b) and gross margin (c) of each N treatment
for Kitahonami. The patch chart below the figure indicates treatment number (N: 1-64),
and the amount of N applied (with four colours) as basal (B), at stem elongation stage(S1), and at flowering (S2). For each box, the height of the box presents the 25-to-75
percentile, whiskers (i.e., the horizontal dashes linked to boxes by vertical lines) present
the 10-to-90 percentile, and the black line in the box is the median.

157

a

b

c

Figure 7.4 Variation of grain yield (a), GPC (b) and gross margin (c) of each N treatment
for Yumechikara. The patch chart below the figure indicates treatment number (N: 164), and the amount of N applied (with four colours) as basal (B), at stem elongation stage(S1), and at flowering (S2). For each box, the height of the box presents the 25-to-75
percentile, whiskers (i.e., the horizontal dashes linked to boxes by vertical lines) present
the 10-to-90 percentile, and the black line in the box is the median.

158

The simulated grain yield, GPC and gross margin (average of 30 years) were plottedagainst the total amount of N applied ranging from 0 to 360 kg N ha-1 (Figure 7.5) The different
data points represent each N treatment (N1-N64). Therefore, the variation within each total N
amount was due to the different proportions of N application as basal, at stem elongation andat flowering.
When the highest grain yield of Kitahonami under each total N level was considered,
the grain yield became relatively stable at the total N rate higher than 160 kg N ha-1 (Figure 7.5
a). The GPC also increased up to 160 kg N ha-1of total N and became stable thereafter. The
highest average gross margins was 722,926 JPY ha-1 at N53 of 160 kg N ha-1 total N (120/40/0
for basal, at stem elongation stage and at flowering).
In case of Yumechikara, the highest grain yield under each total N application level wasincreased up to 280 kg N ha-1 of total N level and then became relatively stable (Figure 7.5b).
Similar to Kitahonami, GPC of Yumechikara also increased up to 120 kg N ha-1of total N leveland became stable thereafter. The highest average gross margin, 984,515.8 JPY ha-1 wasobserved at N62 (280 kg N ha-1 in total split to 120/120/40 at basal, stem elongation stage, andat flowering, respectively) which was higher than that of Kitahonami.
Therefore, it was elucidated that the economically optimum total N application schemes
were 160 kg N ha-1 (120/40/0) and 280 N ha-1 (120/120/40) for soft and hard wheat respectively.

159

Figure 7.5 Variation of grain yield, GPC and gross margin with the amount of N applied.
Primary Y-axis is gross margin (10,000* JPY ha-1) secondary Y-axis is grain yield (t ha1), and GPC (%) and X-axis is the amount of N applied (0 – 360 kg ha-1). Plot “a” denotes

Kitahonami and plot “b” denotes Yumechikara. (grain yield, GPC and, gross marginvalues are average of 30 years under each N treatment (N1-N64))

160

Based on the fertilizer guidelines in Hokkaido, total 180 kg N ha-1 (Basal:40; Stemelongation:100; Flag leaf: 40 ) and 190 kg N ha-1 (Basal: 40; Stem elongation: 90; Flag leaf:60) were selected as the current farmers’ practice (current fertilizer recommendations) for
Kitahonami and Yumechikara respectively (Department of Agriculture, Hokkaido prefectural
government, 2015), and then, additional simulations were conducted to compare the yield andother parameters under the optimal N application scheme elucidated in this scenario analysis
and the current fertilizer practice.
Variation of grain yield, GPC and, gross margin at the lowest (0_0_0), farmers’ practice,optimum and, maximum (120_120_120) N levels for Kitahonami and Yumechikara are shownin Figure 7.6.
According to the simulation results, the median yield at farmers’ N application scheme
was 632.5 and 615.5 g m-2 for both Kitahonami and Yumechikara, respectively (Figures 7.6aand 7.6b). Whereas, the median grain yield at optimum N treatment was 701.7 and 754 g m -2
for Kitahonami and Yumechikara, respectively, or 10.9 and 22.5 % increase in comparison to
the farmers’ N application scheme. Median grain yield at maximum N treatment was similar
to that of optimal N scheme for both varieties.
Median GPC was 9.3 % in Kitahonami and 10.7 % in Yumechikara at the farmers’
practice (Figures 7.6c and 7.6d). 9.3 % GPC of Kitahonami and 10.7% of Yumechikara wasexceeded the ranges of rank A quality bonus window of soft and hard wheat but within theranges of B quality bonus window. At the optimal N application also, GPC of Kitahonami(9.6%) and Yumechikara (10.8%) was within rank B quality bonus window. Median GPC at
maximum N scheme was equal to that of the optimal N level in both varieties.
Median gross margin was 490,521.8 and 725,457.1 JPY ha-1 for Kitahonami andYumechikara respectively, at the farmers’ N application scheme (Figures 7.6e and 7.6f).
However, median gross margins at optimum N scheme was 591,344.1 and 936,539.8 JPY ha-1
161

for Kitahonami and Yumechikara respectively, which is 20.5 % higher in Kitahonami and 29 %higher in Yumechikara compared to that at the farmers’ N application scheme. Since neither
yield nor GPC advantage increased at maximum N scheme compared to that at the optimum
scheme, the gross margin rather tended to decrease owing to the increased cost of fertilizer.

162

b

a

c

B (14 -15.5 %)dA (11.5 -14 %)
B (11.3 -12.5 %)B (10 -11.5 %)A (9.7 -11.3 %)B (8.5 -9.7 %)

e

f

163

Figure 7.6 Variation of grain yield, GPC and, gross margin at the lowest (0_0_0), farmers’
practice (40_100_40 / 40_90_60), optimum (120_40_0 / 120_120_40) and, maximum(120_120_120) N levels for Kitahonami and Yumechikara. Plot “a, c and e” denotes
Kitahonami and plot “b, d and f” denotes Yumechikara. Shaded areas in graph c and ddenote the quality bonus windows. Pink denotes the rank A quality bonus and green
denotes rank B quality bonus. The boxes present the 25-to-75 percentile, whiskers (i.e.,the horizontal dashes linked to boxes by vertical lines) present the 10-to-90 percentile, theblack line in the boxes is median.

164

In Chapter 6 it was demonstrated that the optimal N treatment varied for both hard andsoft wheat grown in Kanto region due to year to year variation of climate. In order to evaluate
the same effect for hard and soft wheat grown in Hokkaido, the optimum N treatment wasdetermined for each year by comparing the gross margin of different treatments. Figure 7.7
shows the change of optimal N treatment over the 30 years together with the changes grain
yield, GPC and gross margin of Kitahonami (a) and Yumechikara (b). Change in averageannual temperature (AAT) and precipitation (AAP) is also plotted in the graphs for comparison.
During the 30 years period both AAT and AAP showed an increasing trend for both Kitami(7.7a) and Sapporo (77b) areas of Hokkaido. The variability of the AAP among the years werevery high. The increasing trend was significant only for AAT (Kitami: y = 0.0366x – 66.6,
P<0.01; Sapporo: y = 0.031x – 52.681, P<0.01).
The most profitable N scheme (optimal) of each year for Kitahonami was mostly stable
and total N rate of most profitable N treatment was 160 kg N ha-1 in 22 years (Figure 7.7a).
Among those 22 years, 20 years had the same N scheme as optimum N scheme elucidated
above, i.e., N 53 (120/40/0, total 160 kg N ha-1 ). Out of 30 years, GPC of 13 years were within
the rank A quality bonus window. The gross margin generally followed the same pattern of thegrain yield.
The optimal N scheme of each year for Yumechikara also tended to stay at around N62(120/120/40, total N rate was 280 kg N ha-1) which was the optimum N scheme elucidated for
Yumechikara. N62 was for 19 years among 30 years of total simulation period. It showed thatout of 30 years, GPC of only 6 years were within the rank A quality bonus window. Similar tothat of Kitahonami, the gross margin generally followed the same pattern of grain yield.

165

a

b

Figure 7.7 Change in optimal N application rate over the 30 years and respective grain
yields, GPCs and gross margins of Kitahonami (a) and Yumechikara (b). Change inannual average temperature (AAT) and precipitation (AAP) is also indicated. Shaded
area in orange is the rank A quality bonus window for both varieties. The patch chart
below the figure with four colours shows the amount of N applied as basal (B), at stem
elongation stage (S1), and at flowering (S2) under each N treatment.

166

7.4 Discussion
7.4.1 APSIM model calibration and validation of wheat growth in Hokkaido
In Chapter 4, calibration and validation of APSIM wheat model for the representative
hard and soft wheat varieties grown in Kanto region was demonstrated. Results presented inthis chapter is the first attempt to calibrate and validate APSIM wheat model for representative
winter wheat varieties in Hokkaido region. Two major varieties in Hokkaido, Kitahonami (softwheat) and Yumechikara (hard wheat) were used for this chapter. At present, 75% of the total
wheat cultivation area is cultivated with Kitahonami and 9.5% of the total area is cultivatedwith Yumechikara, thus, these two varieties represent ca. 85% of the wheat growing area in
Hokkaido (Nihei, 2012; Hokkaido Prefectural Government, 2017). In this chapter, Kitahonami
and Yumechikara were calibrated and validated for the soil and climatic conditions in Kitami(Toumon-Kitami region) and Sapporo (Ishikari plain), respectively. These two sites are thecenters of the two main wheat growing regions in Hokkaido. The calibration and validation
was conducted with the experimental data from published literature of the respective varieties(Table 7.1). The different data sets were used for the calibration and validation. The validation
was done for phenology, grain yield and, GPC at optimum sowing period. The cultivar specificparameters derived for Kitahonami and Yumechikara can be used for the future modelling
research using APSIM model for the Hokkaido region.

167

Validation results showed that, RMSE for grain yield was 75 and 86 g m-2 for
Kitahonami and Yumechikara, respectively. These RMSE values are comparable to the rangeof RMSE for grain yield reported in the literature (40 to 85 g m-2) (Asseng et al., 1998; Asseng
et al., 2000; Chen et al., 2010; Balwinder-Sing et al., 2011). As for GPC, RMSE for Kitahonami
and Yumechikara were 1 and 0.5 %, respectively which was even lower than the range of thereported values (1.5 – 3.2 %) (Asseng et al., 1998; Asseng et al., 2000; Asseng et al., 2002).
RMSE for phenology were 6 and 4 days for Kitahonami and Yumechikara respectively. Preharvest sprouting is one of the major problems in wheat cultivation for Hokkaido region(Yanagisawa et al., 2005). Therefore, accurate prediction of phenology is also vital as adecision support using crop growth models in Hokkaido.
Recently Honda et al. (2014) validated DSSAT model for Hokushin soft wheat variety
for Obihiro region in Hokkaido. They reported 59-75 g m-2 of model mean absolute error(MAE) for grain yield and relatively larger MAE, between 19-27 days for phenology. Thus, incomparison to these results, APSIM model had a lower model error of predicting phenology
over DSSAT model.
Model efficiency (ME) values between 0 and 1 are generally viewed as acceptablelevels of performance, and model performance is regarded as satisfactory if ME > 0.50, good
if ME > 0.65, and very good if ME > 0.75 (Moriasi et al., 2007). In the present result, ME forgrain yield was 0.5 for Yumechikara, which is within the range of that reported by Chen et al.
(2010) and Ahmed et al. 2016 (0.4-0.95), while that for Kitahonami was low (0.08). ME related
to phenology and GPC were respectively, 0.88 and 0.98 for Kitahonami and, 0.94 and 0.3 for
Yumechikara. Considering the fitness of observed and simulated values (Figure 7.1 and 7.2)
and RMSE, the present validation is in a range of acceptable level.

1687.4.2 Economic optima for the nitrogen application in Hokkaido
It was demonstrated in this chapter that the modelling approach applied for Kanto
region in the previous chapters seems to be able to be applied to Hokkaido region. Hokkaido
was selected because it is the largest wheat growing region in Japan having different climaticconditions from Kanto region such as low temperature in winter and snow cover.
Similar to Kanto region, economically optimum nitrogen application scheme was
elucidated by scenario analysis, testing wide range of N application with different combinationsof basal and split applications. The results would also provide guidance to increase profitability
for the wheat farmers in the region by increasing yield and obtaining the benefits of quality
bonus. Thirty years of daily historical weather data was used to test the effect of long-termclimate variability on the effectiveness of each scenario.
The economically optimum N application rate derived from simulation was 280 and
160 kg N ha-1 in total for Yumechikara and Kitahonami wheat varieties respectively. The split
N rates were 120-120-40 for hard wheat, and 120-40-0 for soft wheat (kg N ha-1 for basal, split
at stem elongation stage and at flowering stage). With this optimum N application scheme,
farmers can obtain maximum gross margin of 984,515.8 JPY for hard wheat and 722,926 JPY
ha-1 for soft wheat.
The recommended fertilizer level in Hokkaido for Yumechikara is 190 kg N ha-1 (Basal:
40; Stem elongation: 90; Flag leaf: 60), (Department of Agriculture, Hokkaido prefectural
government, 2015). Therefore, in comparison to the derived optimal N application rate, thecurrent fertilizer recommendation is lower than that of the optimal rate (280 kg N ha-1).
However still the gap is lower than that was found in Kanto region for Yumeshiho (i.e., Optimal
N rate is 280 kg N ha-1 and the current fertilizer recommendation is 100 kg N ha-1).
In the case of Kitahonami, the current fertilizer recommendation is 180 kg N ha-1
(Basal: 40; Stem elongation: 100; Flag leaf: 40), (Department of Agriculture, Hokkaido
169prefectural government, 2015). The elucidated optimal N application for Kitahonami is 160 kg
N ha-1. Therefore, it can be said that with the current fertilizer recommendation level, theoptimum N requirement can be met and current fertilizer recommendation is slightly higher
than that of elucidated optimum N application rate.
According to the simulations results, Kitahonami is reaching to the saturation of thegrain yield at around 160 kg N ha-1 fertilizer rate, therefore, there is no any economic gain
could be achieved increasing the fertilizer rate beyond 160 kg N ha-1 fertilizer rate (Figure 7.5a).
This is different from Yumechikara and other two varieties from Kanto region (Yumeshiho andAyahikari). For them, yield saturation happed at higher N application rate (280 kg N ha -1)(Figure 7.5b and 6.3). The possible reason for this difference with Kitahonami may be due toits higher nitrogen use efficiency. According to the literature, Kitahonami is characterised byhigher grain yield and lower GPC (Yanagisawa et al., 2007 as cited in Fueki et al., 2015b). The
higher grain yield is attributed to the larger sink capacity (higher number of grains per year andthousand grain weight) (Kasajima et al. 2016). Also, according to the Kasajima et al. (2016),
the erect green leaves of Kitahonami can absorb radiation more effectively, leading to a higher
net assimilation rate in the later grain filling phase and higher dry-matter production after the
milk-ripe stage. The difference in performances of the two varieties in the model is expressedby cultivar parameters, i.e., “vernalization sensitivity”, “photoperiod sensitivity”, and “thermal
time from beginning of grain filling to maturity” were lower for Kitahonami than Yumechikara
whereas “maximum specific leaf area for delta LAI” and “potential grain filling rate” are higher
for Kitahonami than Yumechikara (Table 7.3). Owing to these differences in cultivar
parameters Kitahonami showed shorter growth duration, higher leaf area, shorter grain filling
period and higher rate of grain filling compared to Yumechikara.
Average wheat yield in Hokkaido region is reported to be 440 g m-2 (MAFF, 2014).
However, in the limited number of recent field experiments, wheat yield related to current
170fertilizer recommendation rates were reported to be 579 to 634 g m-2 for Kitahonami(Tsukamoto et al., 2017; Kasajima et al., 2016) and 617 g m-2 (Tsuji et al 2010) for
Yumechikara. At the optimal N treatment derived from the scenario analysis, simulated yield
showed to be increased (i.e., 701 and 754 g m-2, respectively for Kitahonami and Yumechikara)
than the average wheat yield in Hokkaido and the reported yields under above mentioned
experimental conditions.
The simulated yield levels under optimal N treatment can be verified with existing
reports in which nearly similar N applications were used; Fueki et al. (2015a) reported 731 g
m-2, grain yield with 160 kg N ha-1 N application for Kitahonami. Yoshihira et al. (2012) had
reported, 650 g m-2, grain yield with 220 kg N ha-1 N application for Yumechikara. Therefore,
these field experimental results imply the reliability of the optimum N scheme derived fromthe scenario analysis.
The optimal N rate derived from the simulation analysis of this research suggested theadvantage of increasing the total N application up to 160 kg N ha-1 and 280 for Kitahonami andYumechikara, respectively, of which 120 kg N ha-1 to be applied as basal. This is in contrast to
the fertilizer recommendations for Hokkaido (40 kg N ha-1 basal application). Watanabe (2012)suggested that any N amount applied higher than 40 kg N ha-1 would be remained in the soil
unutilized by the plants and will be leached by the snow melt before plant started growing
rapidly. Hence, these results have shown the need of further improvement in APSIM soil waterand soil N modules to capture the soil water and N dynamics under snow cover and after snow
melting (which is not considered by the current APSIM wheat model).
The highest gross margin which was resulted with optimum N scheme of Yumechikara(hard wheat) were higher than that of Kitahonami (soft wheat) owing to the higher quality
bonus offered for hard wheat (Figures 7.5 and 7.6). With respect to the GPC contribution forgross margin, under the optimum N treatment, both Yumchikara and Kitahonami from171

Hokkaido region resulted GPC belonged to the rank B quality bonus range for hard wheat (10– 11.5 %) and soft wheat (8.5-9.7 %) which gave a 5.8% and 8.4 % less quality bonus comparedto the premium quality bonus (rank A) respectively, for hard and soft wheat.
The simulation results showed that yearly optimal N application scheme tend to berelatively stable during 30 years period in Hokkaido varieties and it was at or closer to the
optimal N scheme derived based on the average gross margin of 30 years, which is in contrast
to the optimal N scheme for the varieties of Kanto which was more changeable for year to year
owing to the inter-annual climate fluctuation (Figure 7.7).

7.5 Conclusions
The results of this chapter showed the implications of applying/extending the modelling
approach for fertilizer recommendation of wheat in the Hokkaido region. The results of thissimulation study at the representative sites indicated that economically optimum rate of total
N application for Yumechikara (hard wheat) was 280 kg N ha-1. The respective split N rates
were 120-120-40 kg N ha-1 at sowing, at stem elongation stage and at flowering, respectively.
With this optimum N treatment, the producers can obtain by 29 % higher median gross margin(one out of 2 year chance) than the gross margin which can be obtained with current fertilizer
recommendations. The elucidated economically optimum N rate for Kitahonami (soft wheat)
was lower than that of Yumechikara which was 160 kg N ha-1 (120-40-0 kg N ha-1 at sowing,at stem elongation stage and at flowering, respectively). The median gross margin increased
was by 20.5 % for Kitahonami with that optimum N treatment. Thus, the use of APSIM modelfor decision support on N management helped to elucidate an optimum N application regime
for typical hard and soft wheat varieties grown in Hokkaido region. For the future research it
was suggested to include model improvements in APSIM to capture the soil N and water
dynamics under snow cover and during the period of snow melt.
172

Chapter 8General Discussion and Conclusions
This study focused on optimizing productivity and quality of wheat through modelling
approach. Thus, this study consisted of three main parts: field experiments, model applicationand scenario analysis. The purpose of the field experiments using both hard and soft wheat
varieties grown in Kanto area was to elucidate the effect of N management and late sowing
conditions on yield, yield components, grain protein concertation, and seed emergence at low
temperature. The model application part is comprised of parameterization and validation of
APSIM model to the target wheat growth conditions, and in addition, improving the model tobe better applied for late sowing conditions in Kanto region. The scenario analysis is comprisedof N management decision support for both hard and soft wheat grown in Kanto region and,
finally it was also tested the possibility of applying this modelling approach to the largest wheat
producing prefecture, Hokkaido.
The objective of this chapter to discuss the implications that can be drawn from thisstudy that would help to improve the wheat productivity in Japan and to provide insights on
wheat research perspectives.

1738.1 N management optima elucidated from modeling to improve the wheat productivity
Scenario analysis was conducted to find out the economically optimum N application
scheme by testing the wider range of N treatments with different combinations of basal andsplit applications.
Table 8.1 summarizes the current N management scheme, elucidated optimal N
management scheme for Kanto and Hokkaido regions and, percentage change of median grossmargin that can be expected with optimal N scheme elucidated in this study.
The results of this simulation study indicated that economically optimum rate of total
N application for both Yumeshiho (hard wheat) and Ayahikari (soft wheat) from Kanto region
was 280 kg N ha-1 (Chapter 6, Figure 6.3). Split N rates were, however, different: 120-80-80
kg N ha-1 at sowing, at stem elongation stage and at flowering, respectively for Yumeshiho,
and 120-120-40 for Ayahikari. With these optimum N treatments, the producers can obtain by
84.8 and 145.6 % higher median gross margin (one out of 2 year chance) for Yumeshiho andAyahikari, respectively than that can be obtained by current fertilizer recommendations.
(Figure 6.4 of Chapter 6, Table 8.1). The simulation results showed that yearly optimal N
application scheme tend to change to some extent due to year to year climate variation during
the 55 years period in both varieties. However during the average years it was at or closer tothe optimal N scheme derived based on the average gross margin of 55 years (280 kg N ha-1)(Chapter 6, Figure 6.5).
The economically optimum rates of total N application for Yumechikara (hard wheat)from Hokkaido region was 280 kg N ha-1 split to 120-120-40 kg N ha-1 at sowing, at stem
elongation stage and at flowering, respectively. And that for Kitahonami was 160 kg N ha-1 2040-0 kg N ha-1 (Chapter 7, Figure 7.5). With these optimum N treatments, the producers canobtain by 29 and 20.5 % higher median gross margin (one out of 2 year chance) compared tothe current level for Yumechikara and Kitahonami, respectively (Figure 7.6 of Chapter 7, Table
1748.1). The simulation results showed that yearly optimal N application scheme tend to berelatively stable during the 30 years period in both varieties and it was at or closer to the optimal
N scheme derived based on the average gross margin of 30 years (160 and 280 kg N ha-1)(Chapter 7, Figure 7.7).

Table 8.1 Percentage change in median gross margin due to optimal N treatment for hardand soft wheat grown in Kanto and Hokkaido regions.

RegionVariety

Kanto

Ayahikari (soft wheat)
Yumeshiho (hard wheat)

Current N
management
scheme in theregion(kg N ha-1)
1
100 (80/20/0)
100 (80/20/0)1

Hokkaido Kitahonami (soft wheat) 180 (40/100/40)2
2
Yumechikara (hard wheat) 190 (40/90/60)
1Basal/Stem elongation/flowering

2

Basal/Stem elongation/flag leaf

Economically% increase of median
optimumgross margin
N management
due to optimal N
schemescheme(kg N ha-1)
1
280 (120/120/40)145.6
1280 (120/80/80)
84.8
160 (120/40)1
1
280 (120/120/40)

20.5
29.0

Thus, the use of APSIM model for decision support on N management helped toelucidate an optimum N application regime for typical hard and soft wheat varieties grown in
Kanto and Hokkaido region even taking the long-term climate variation into account.

8.2 Implications of modelling approach on uplifting current yield levels
The Figure 8.1 shows the relationship between the amount of fertilizer applied and theaverage yield reported in Japan, European countries and New Zealand and the simulated
median grain yield resulted from optimal N scheme derived with the scenario analysis.
It showed that amount of fertilizer applied in European countries and New Zealand are
higher than that of Japan (FAO, 2016; FAO, 2002; Han and Dijk, n.d.; HGCA, 2009; Grinsven
et al., 2012; Jones, 2013). Conventional lower fertilizer rates in Japan especially in Kanto
175

region is usually attributed to the risk avoidance associated with lodging. Also, the national
average wheat yield in Japan (4.1 t ha-1) is lower than that in other major wheat producing
countries indicated in the Figure 8.1 as was discussed in Chapter 6 (yield values are the three
years mean value from 2014 to 2016; FAO, 2016).
As shown in Figure 8.1, the optimal N schemes elucidated from the modelling approach
resulted in the increase of median yields of hard and soft wheat grown in Kanto and Hokkaido
regions to the close levels of the yields of high yielding countries. The appropriate N
management would be a promising approach to improve the wheat productivity in Japan, andmodelling approach combined with field experimental knowledge would be a good method forproviding decision support.

Figure 8.1 National average wheat grain yields (data points with close circles) Vs.
Fertilizer application rates in Japan, France, Netherlands, New Zealand and United
Kingdom and median grain yields of hard and soft wheat cultivars (data points with open
circles) from Kanto and Hokkaido regions, resulted from simulated optimal N schemes .

176

8.3 Conclusions
This study focused on optimizing productivity and quality of wheat through modelling
approach. Thus, the N management was considered as the main agronomical technology toevaluate the effects on increasing the grain yield and controlling GPC for the expected quality
levels. Late sowing was also considered for the Kanto region since it is a concerned issue for
producers in the region. Based on that, field experiments were conducted in the Kanto region
to generate the field experiment knowledge. For the Hokkaido region, the data from thepublished research was obtained. Then APSIM crop model was calibrated and validated forboth regions to get the confidence about the model accuracy and suitability of reproducing thefield experiment knowledge in the simulations. With the acceptable validation results, themodel was used to conduct scenario analysis to elucidate the economically optimum N schemes
for the hard and soft wheat grown in Kanto and Hokkaido regions by taking the historicalclimate variation into account. Thus, by the modelling approach, this research demonstrated
that, by optimum N management current average yield level of Japan could be increased to thecloser level of that in the high yielding countries in the world with reasonable level of GPC,
and there by increase the profitability of the producers.
This is the first study conducted, using a modelling approach to derive a decision
support to improve the wheat productivity and quality in Japan considering both grain yield
and GPC, to the best of my knowledge.
In this study, a model improvement was conducted with the inclusion of the algorism
of reducing the plant density when the emergence was delayed at the late sowing conditions.
The new algorithm was coded incorporating a quadratic function obtained from the field
experimental results. To best of my knowledge, this is the first report of improving APSIM
model by integrating an empirical model to correct the plant density at emergence due to the
low temperature at emergence.
177Concerning future research, it is suggested to extend and test this modelling approach
mainly for the Kyushu and other wheat growing regions in Japan (with the validation for N
movement in Japanese soil as well) and, to test the scenario analysis with generated forecasted
weather data for forthcoming cropping season, for predicting optimal N management scheme,
optimal sowing time, and phenology (flowering and maturity time) for coming season as a realtime decision support for the producers.

178

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