Agronomic Management of Corn Using Seasonal Climate Predictions, Remote Sensing and Crop Simulation Models

AGRONOMIC MANAGEMENT OF CORN USING SEASONAL CLIMATE PREDICTIONS, REMOTE SENSING AND CROP SIMULATION MODELS

Prakash Kumar Jha

A DISSERTATION

Submitted to Michigan State University in partial fulfillment of the requirements for the degree of

Crop and Soil Sciences - Doctor of Philosophy

2019

ABSTRACT

AGRONOMIC MANAGEMENT OF CORN USING SEASONAL CLIMATE PREDICTIONS, REMOTE SENSING AND CROP SIMULATION MODELS

Prakash Kumar Jha

Management decisions in corn (Zea mays mays L) production are usually based on specific growth stages. However, because of climate and weather variability, phenological stages vary from season to season across geographic locations. This variability in growth and phenology entails risks and quantifying it will help in managing climate related risks. Crop simulation models can play a significant role in minimizing these risks through designing management strategies; however, they are not always accurate. Remote sensing observations and climate predictions can improve the accuracy in managing time bound climate-sensitive decisions at larger spatiotemporal scale. However, there is also a disconnect between climate forecasts and crop models. The unavailability of downscaling tool that can downscale rainfall and temperature forecasts simultaneously make this task more challenging. To address these knowledge gaps, this dissertation consists of three studies focused on interdisciplinary approaches to agronomic management of corn.

In the first study, we calibrated and validated genetic coefficients of CERES-Maize using field data from the Michigan corn performance trials. Multiple methods of estimating genetic coefficients GENCALC (Genotype Coefficient Calculator), GLUE (Generalized Likelihood

Uncertainty Estimate), and NMCGA (Noisy Monte Carlo Genetic Algorithm) were evaluated and ensembled to estimate more reliable genetic coefficients. The calibrations were done under irrigated conditions and validation under rainfed conditions. The results suggested that

ensembled genetic coefficients performed best among all, with d-index of 0.94 and 0.96 in calibration and validation for anthesis and maturity dates, and yield.

In the second study, simulated growth stages from the calibrated crop model were used to develop site-specific crop coefficients (kc) using ensembled ET and reference ET from the nearest weather station. ET from multiple models were ensembled and validated with the measured ET from eddy-covariance flux towers for 2010 – 2017. Results suggest that the ensembled ET performed best among all ET models used, with highest d-index of 0.94.

Likewise, the performance of the newly derived kc-curve was compared with FAO-kc curve using a soil water balance model. Then, the derived region-specific Kc-curve was used to design irrigation scheduling and results suggest that it performed better than FAO Kc-curve in minimizing the amount irrigation while maintaining a prescribed allowable water stress.

The third study used the calibrated crop model to simulate anthesis using downscaled seasonal climate forecasts. The predicted anthesis and downscaled seasonal climate forecasts were used to develop risk analysis model for ear rot disease management in corn. In this study an innovative downscaling tool, called FResamplerPT, was introduced to downscale rainfall and temperature simultaneously. The results suggest that temperature and relative humidity are better predictors (combined) as compared to temperature and rainfall (combined). With this risk analysis model, growers can evaluate and assess the future climatic conditions in the season before planting the crops. The seasonal climate information with the lead-time of 3 months can help growers to prepare integrated management strategies for ear rot disease management in maize.

This thesis is dedicated to the supreme personality of Godhead, my lovely family and friends for all the support and kindness they have given me.

ACKNOWLEDGMENTS

I acknowledge the great power, who has paved the way on which I have walked so far.

As a prelude to my thanksgiving, at first I wish to thank and bow my head before Lord Almighty, for blessing me with all his grace to successfully complete my studies and research endeavor at

Michigan State University.

At this moment of accomplishment, it is my great privilege to express a deep sense of gratitude to my advisor Dr. Amor V. M. Ines. As the chair of my advisory committee, he guided me as a teacher, helped me like a friend and cared me as a guardian. I am also thankful for every bit of his valuable guidance, personal attention, constructive criticism, immense patience, untiring help, wholehearted support and encouragement throughout the period of my study and preparation of this manuscript.

I am sincerely thankful to Dr. Amor Ines for the funding support during my research program. I specifically acknowledge the funding support from IFPRI, Columbia University,

NASA- SERVIR, Chubu University, USAID-Phillipines, ListenField, MSU AgBioResearch and

Corn Marketing Program of Michigan (CMPM) for the entire study. I also acknowledge College of Agricultural and Natural Resources (CANR), Michigan State University for the fellowships during my course, which helped me in completing my work smoothly. I am also thankful to

South Asia Studies centre at Michigan State University for offering me the “Dr. Delia-koo

Global Student Scholarship” award, which helped me during my research.

I express my sincere thanks and gratefulness to Dr. Maninder Singh, for encouraging me to undertake this study and providing me with his precious advice, necessary guidance and help rendered during my field studies. I express my sincere and heartiest thanks to Dr. Bruno Basso,

Dr. Jeff Andresen and Dr. Pouyan Nejadhashemi for, ever-ready support and guidance all the

vi time during my research work as the members of my advisory committee and specially for their most valuable help rendered in relation to planning, execution and analysis of this work.

I owe a great deal of gratitude towards my teachers and scientists in the department, for their valuable suggestions and constant encouragement during my course of research. Specially, I would like to acknowledge Dr. Jiquan Chen, Dr. Michael Abraha, Dr. Joshua Fisher, Dr.

Younsuk Dong, Steve Miller, and Lyndon Kelley for their invaluable contributions in shaping my dissertation. My sincere thanks go to Dr. Chirag K. Vyas, Mr. Suyog Chaudhari, Mr.

Eeswaran Rasu and Mr. Abhijit Abhishek for their timely help and encouragement.

No words can describe the unending love and constant moral support from my parents and lovely wife, Mrs. Supriya Jha. I affectionately cherish the blessings and good wishes of my beloved parents, my loving siblings, and all my relatives who were the constant sources of inspiration to me from miles apart.

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TABLE OF CONTENTS

LIST OF TABLES ...... x

LIST OF FIGURES ...... xii

KEY TO ABBREVIATIONS ...... xvi

1. INTRODUCTION ...... 1

2. INTRODUCTION TO METHODOLOGY AND RESULTS ...... 6

3. ESTIMATING GENETIC COEFFICIENTS OF CERES-MAIZE TO SIMULATE PHENOLOGY AND YIELD OF MAIZE IN MICHIGAN ...... 9 3.1 Introduction ...... 9 3.2 Materials and Methods ...... 14 3.2.1 Study area ...... 14 3.2.2 CERES-Maize model ...... 16 3.2.3 Input data for CERES-Maize ...... 17 3.2.4 Calibration methods ...... 21 3.2.5. Ensembling approach ...... 28 3.2.6 Validation and statistical analysis ...... 29 3.3. Results and Discussion ...... 30 3.3.1 Calibration...... 30 3.3.2 Potential yields from calibrated genetic coefficients ...... 38 3.3.3 Validation ...... 39 3.4 Summary and Conclusions ...... 48 3.5 Acknowledgment ...... 49

4. ESTIMATION AND VALIDATION OF REMOTELY SENSED EVAPOTRANSPIRATION FOR THE DEVELOPMENT OF CROP COEFFICIENTS OF MAIZE AND IRRIGATION SCHEDULING ...... 50 4.1 Introduction ...... 50 4.2 Materials and Methods ...... 53 4.2.1 Study Area...... 53 4.2.2 Data Collection...... 55 4.2.3 ET models descriptions ...... 60 4.2.4 Ensembling approach ...... 63 4.2.5 Kc Curve and Irrigation Scheduling ...... 65 4.2.6 Development of Kc-curve ...... 66 4.2.7 Validation of Kc-curve...... 67 4.2.8 Statistical evaluation of model performance ...... 68 4.3 Results and Discussion ...... 69 4.3.1 ET Estimation and comparison ...... 69 4.3.2 Ensembling ET estimates ...... 79

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4.3.3 Development of Kc – Curve ...... 85 4.3.4 Validation of Kc – Curve ...... 90 4.3.5 Irrigation Scheduling of Maize: a comparison of standard FAO- Kc and the derived Kc ...... 95 4.4 Summary and Conclusions ...... 96 4.5 Acknowledgements ...... 98

5. DOWNSCALING SEASONAL RAINFALL AND TEMPERATURE FORECASTS TO DEVELOP A RISK ANALYSIS MODEL FOR EAR ROT DISEASE MANAGEMENT IN MAIZE ...... 99 5.1 Introduction ...... 99 5.2 Materials and Method...... 103 5.2.1 Study Area...... 103 5.2.2 Data collection ...... 104 5.2.3 Downscaling rainfall and temperature: software description ...... 109 5.2.4 Crop model setup ...... 111 5.2.5 Developing risk indicators...... 111 5.2.6 Statistical analysis ...... 112 5.3 Results and Discussions ...... 113 5.3.1 Predicting phenology ...... 113 5.3.2 Risk Probabilities using Forecast data ...... 115 5.3.3 Validation of risk analysis model ...... 127 5.4 Summary and Conclusions ...... 131 5.5 Acknowledgement ...... 132

6. CONCLUSIONS ...... 133

7. FUTURE RESEARCH RECOMMENDATIONS ...... 136

REFERENCES ...... 138

LIST OF TABLES

Table 3. 1. Location of the maize fields under Michigan maize Performance Trials (MMPT). .. 15

Table 3. 2. Summary of weather data (2017 and 2018) during the crop season (May-October) for selected locations...... 17

Table 3. 3. Descriptions of field observations used in the study...... 19

Table 3. 4. Descriptions of genetic coefficients in CERES-Maize...... 22

Table 3. 5. Representations of phenology and growth parameters in NMCGA...... 28

Table 3. 6. Genetic coefficients estimated by GENCALC, GLUE, NMCGA and Ensembling approach in 2017...... 31

Table 3. 7. Performance of CERES-Maize using estimated parameters by GENCALC, GLUE, NMCGA and ensembling approach during calibration in 2017...... 32

Table 3. 8. Genetic coefficients estimated by GENCALC, GLUE, NMCGA_SD, NMCGA_NO_SD and Ensembling approach in 2017 and 2018...... 33

Table 3. 9. Performance of CERES-Maize using parameters estimated by GENCALC, GLUE, NMCGA and ensembling approach during calibration in 2017 and 2018...... 34

Table 3. 11. Soil root growth distribution factor (SRGF) of the sandy loam soil used in calibration and validation...... 41

Table 3. 12. Soil root growth distribution factor (SRGF) of the loam soil used in calibration and validation...... 41

Table 3. 13. Performance of CERES-Maize using parameters estimated by GENCALC, GLUE, NMCGA and ensembling approach during validation at rainfed locations with the revised soil profiles...... 45

Table 4. 1. Statistical evaluation of seasonal ET estimates for 2010-2017 among the models and flux tower during the growing season (May- October) at Marshall and KBS maize farms...... 72

Table 4. 2. DSSAT based simulated and updated length of crop development stages used for ensembling purposes ...... 79

Table 4. 3. Crop coefficients as an input for soil water balance model ...... 90

Table 4. 4. Statistical evaluation of soil water balance model performance under three different rainfall inputs by comparing observed and simulated total available water in root zone (mm). .. 91

Table 4. 5. Available water holding capacity of soil and percent capacity filled at different depth for the sandy clay loam (Jagtap et al., 2004) ...... 95

Table 5. 1. Monthly averages of weather variables (1981-2010) in the study area. 105

Table 5. 2. Field observation and U2U estimation of silking stage at Saginaw, Huron and Montcalm in 2018...... 109

Table 5. 3. Statistical significance of forecast risk probabilities compared to climatological risks...... 126

LIST OF FIGURES

Figure 2. 1. The general flowchart for the methodology of this dissertation...... 7

Figure 3. 1. Locations of maize performance trials in Michigan...... 14

Figure 3. 2. Weather data (Rainfall, solar radiation, maximum and minimum temperature)for cropping season (May-October) for 2017 (Source: MSU Enviroweather) ...... 20

Figure 3. 3. Weather data (Rainfall, solar radiation, maximum and minimum temperature) for cropping season (May-October) for 2018. (Source: MSU Enviroweather) ...... 20

Figure 3. 4. GENCALC-CERES-Maize working flowchart (Adnan et al., 2019)...... 23

Figure 3. 5. CERES-Maize performance after calibration of GENCALC, GLUE, NMCGA_SD, NMCGA_NO_SD, arithmetic and weighted averaging for 2017 and 2018...... 37

Figure 3. 6. Potential yields and observed yield for 2017 and 2018 for all maize trial locations using weighted averaging of genetic coefficients...... 38

Figure 3. 7. Model performance ((root depth, water and nitrogen stress andyield (a)) and (root length density (b,c))) in 2017 at Washtenaw (rainfed) under old (b) and revised soil (c) profiles...... 43

Figure 3. 8. Model performance ((Root depth, water and nitrogen stress, yield (a)) and (root length density (b,c))) in 2017 at Cass (irrigated) under old (b) and revised soil (c) profiles...... 45

Figure 3. 9. CERES-Maize performance for validation of calibrated coefficients of GENCALC, GLUE, NMCGA_SD, NMCGA_NO_SD, arithmetic and weighted averaging for 2017 and 2018...... 47

Figure 4. 1. Eddy covariance towers at the maize fields ...... 54

Figure 4. 2. Mean reference ET (2010-2017) from the nearest weather station (Hasting, Michigan) in the study area...... 56

Figure 4. 3. Rain gauge (a) and soil moisture sensors (b) at the maize field in Manchester, Michigan...... 57

Figure 4. 4. Crop coefficient curve from FAO and the curve created from CERES-Maize growth stages...... 65

Figure 4. 5. Conceptualization of developing satellite derived crop coefficient curve...... 66

Figure 4. 6. Bounding box for remote sensing analysis in the study area ...... 69

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Figure 4. 7. ET map (SEBAL, PT-JPL and MOD16A2) for a representative day during growing season (June 26, 2012) ...... 70

Figure 4. 8. Comparison of estimated ET (a) MOD16A2, (b) SEBAL, (c) PT-JPL, (d) FAO with the eddy covariance measurements at the KBS maize field for 2012...... 71

Figure 4. 9. Temporal variation of monthly ET among the methods (b) FAO (c) SEBAL, (d) PT- JPL, (e) MOD16A2 and ground observation of flux tower (a) during growing season (May- October) at KBS maize farm ...... 75

Figure 4. 10. Temporal variation of seasonal ET among the models and ground observations from flux towers during growing season (May- October) at KBS (a) and Marshall (b) ...... 76

Figure 4. 11. Inter model comparison of seasonal ET among the models and ground observations from the flux tower during growing season (May- October) at KBS ...... 77

Figure 4. 12. Inter model comparison of seasonal ET among the models and ground observations from the flux tower during growing season (May- October) at Marshall ...... 78

Figure 4. 13. Ensemble ET estimates by stage based Inverse Distance Weighting (IDW) for 2012 with a reference of ground observations from the flux tower during growing season (May- October) at KBS...... 80

Figure 4. 14. Variation in time series of seasonal ET (monthly average) estimates among the models and ground observations from flux tower during growing season (May- October) at KBS (ET-Average is from ensemble method)...... 81

Figure 4. 15. Variation in time series of seasonal ET (monthly average) estimates among the models and ground observations from flux tower during growing season (May- October) at Marshall (ET-Average is from ensemble method)Moreover, the interannual varaition in ET .... 82

Figure 4. 16. Variation in ensembled daily ET estimates for crop season (May- October) for 2010-2017 at KBS...... 83

Figure 4. 17. Variation in ensembled daily ET estimates for crop season (May- October) for 2010-2017 at Marshall ...... 84

Figure 4. 18. Temporal variation in crop coefficient curve of Maize derived by ensemble ET for 2010-2017 at KBS...... 86

Figure 4. 19. Temporal variation in crop coefficient curve of Maize derived by ensemble ET for 2010-2017 at Marshall ...... 87

Figure 4. 20. Smooth fitted crop coefficient curve of Maize derived by ensemble ET for 2010- 2017 at KBS and Marshall ...... 88

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Figure 4. 21. Averaged (2010-2017) crop coefficient curve of Maize at KBS and Marshall ...... 89

Figure 4. 22. Observed rainfall and irrigation amount with (a) volumetric soil moisture at different depth (15, 30, 45, 60 and 90cm); (b) total available water in root zone (90 cm) ...... 92

Figure 4. 23. Observed and simulated total available water (TAW) in root zone with three rainfall input (a) rain gauge (b) weather station (c) reanalyzed product from NASA-POWER, and irrigation value reported by grower at Manchester maize field for four Kc-curve (FAO, KBS, Marshall, and average) in 2019 maize growing season...... 93

Figure 4. 24. Simulated total available water in root zone using water balance model for two crop coefficients (a) standard FAO- Kc (b) Kc derived in 2019 maize growing season with 60 % set capacity of available water...... 94

Figure 4. 25. Simulated total available water in root zone using water balance model for standard FAO- Kc using farmers’ irrigated practice in the maize growing season of 2019 with 60 % set capacity of available water...... 96

Figure 5. 1. Study area having high ear rot disease severity in 2018...... 103

Figure 5. 2. Observed weather variables for 2017 and 2018 during crop growing season in the study area...... 104

Figure 5. 3. Illustration of (a) historical and climate prediction utility for management decisions (b) prediction horizon of seasonal climate forecast and usability for farm related decisions..... 107

Figure 5. 4. Framework for downscaling probabilistic seasonal rainfall (P) & temperature (T) forecasts that preserves forecast probabilities of rainfall p (P) and temperature p (T) (Ines et al., 2018) ...... 110

Figure 5. 5. Predicted anthesis for all three locations for 2018 forecast (a), warm and humid forecast (b), warm and dry forecast (c), cool and wet forecast (d), and cool and dry forecast (e)...... 114

Figure 5. 6. Risk probabilities for temperature (a-c), relative humidity (RH) (d-f) and rain (g-i) at Saginaw (a,d,g), Huron (b,e,h), and Montcalm (c,f,i) for 2018 forecast and climatology (1981- 2010). The red arrow indicates predicted mean anthesis days ...... 117

Figure 5. 7. Risk probabilities for combination of temperature and relative humidity and combination of temperature and rain at Saginaw (a), Huron (b), and Montcalm (c) for 2018 forecast and climatology (1981-2010). The red arrow indicates predicted mean anthesis days 118

Figure 5. 8. Risk probabilities for temperature at Saginaw, Huron and Montcalm for (a) warm and humid, (b) warm and dry, (c) cool and wet, (d) cool and dry forecast scenarios and

xiv climatology (1981-2010) for all three locations. The red arrow indicates predicted mean anthesis days...... 121

Figure 5. 9. Risk probabilities for relative humidity (RH) at Saginaw, Huron and Montcalm for (a) warm and humid, (b) warm and dry, (c) cool and wet, (d) cool and dry forecast scenarios and climatology (1981-2010) for all three locations. The red arrow indicates predicted mean anthesis days...... 122

Figure 5. 10. Risk probabilities for rain at Saginaw, Huron and Montcalm for (a) warm and humid, (b) warm and dry, (c) cool and wet, (d) cool and dry forecast scenarios and climatology (1981-2010) for all three locations. The red arrow indicates predicted mean anthesis days...... 123

Figure 5. 11. Risk probabilities for combination of temperature and relative humidity (T&RH) at Saginaw, Huron and Montcalm for (a) warm and humid, (b) warm and dry, (c) cool and wet, (d) cool and dry forecast scenarios and climatology (1981-2010) for all three locations. The red arrow indicates predicted mean anthesis days...... 124

Figure 5. 12. Risk probabilities for combination of rain and temperature and rain (R&T) at Saginaw, Huron and Montcalm for (a) warm and humid, (b) warm and dry, (c) cool and wet, (d) cool and dry forecast scenarios and climatology (1981-2010) for all three locations. The red arrow indicates predicted mean anthesis days...... 125

Figure 5. 13. DON concertation at each location in 2017 and 2018; Source: Blaine et al., (2018) ...... 127

Figure 5. 14. Risk factor of weather variable(s) in predicting ear rot disease for Saginaw, Huron and Montcalm (a) relative humidity (RH), (b) temperature (T), (c) rainfall (R), (d) RH & T, (e) R & T in 2017 and 2018...... 130

KEY TO ABBREVIATIONS

ADAP: Anthesis Days After Planting

AN: Above Normal

APSIM: Agricultural Production Systems sIMulator

ALEXI: Atmosphere-Land Exchange Inverse

AVHRR: Advanced Very High Resolution Spectroradiometer

BAITSSS: Backward‐Averaged Iterative Two‐source Surface temperature and energy balance Solution

BN: Below Normal

CCP: Cloud Climatology Project

CERES: Crop Environment Resource Synthesis

CRM: Comparative Relative Maturity

CRP: Conservation Reserve Program

CSM: Cropping System Model

DSSAT: Decision Support System for Agro-technology Transfer

DAP: Days after planting

DON: Deoxynivalenol

.dat: data file

ERI: Ear Rot Incidence

E: Environment

EC: eddy covariance

ECOSTRESS: ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station

ENVI: Environment for Visualizing Images

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ET: Evapotranspiration

ETa: Actual evapotranspiration

ETcrop: Actual crop evapotranspiration

ETref: Reference Evapotranspiration

ETRS: Evapotranspiration from remote sensing

FAO: Food and Agriculture Organization of the United Nations

FResamplerPT: Forecast Resampler Precipitation and Temperature

FYM: Farm Yard Manure

G: Genotype

GA: Genetic algorithm

GC-MS: Gas Chromatography- Mass Spectrometry

GDD: Growing Degree Days

GDDF: Growing Degree Days (in ᵒF)

GDDC: Growing Degree Days (in ᵒC)

GENCALC: Genotype Coefficient Calculator

GHCN: Global Historical Climatology Network

GLUE: Generalized Likelihood Uncertainty Estimate

GMAO: Global Modeling and Assimilation Office

GPCP: Global Precipitation Climate Project

GREEEN: Generating Research and Extension to meet Economic and Environmental Needs

GRIB: General Regularly-distributed Information in Binary form

Ha: hectare

HDF: Hierarchical Data format

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.hdr: Header file

HWAM: Yield at harvest maturity

IDW: Inverse Distance Weightage

IPCC: Intergovernmental Panel on Climate Change

ISLSCP: International Satellite Land-Surface Climatology Project

IR: Infrared

JPL: Jet Propulsion Laboratory

K: Potassium

Kc: Crop coefficients

Kcr: Crop coefficients derived through reflectance

KBS: Kellog’s Biological Station

Kg: Kilogram

M: Management

MBE: Mean Bias Error

MDAP: Maturity Date Days After Planting

MDARD: Michigan Department of Agriculture and Rural Development

METRIC: Mapping EvapoTranspiration at high Resolution with Internalized Calibration

MMPT: Michigan Maize Performance Trial locations

MOD09GA: Daily surface reflectance product

MOD11A1: Land surface temperature & emissivity product

MOD16A2: MODIS Global Evapotranspiration Project

MODIS: Moderate Resolution Imaging Spectroradiometer

MRT: MODIS Reprojection Tool

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MCTK: MODIS conversion toolkit

MSU: Michigan State University

N: Nitrogen

NN: Near-Normal

NASA: National Aeronautics and Space Administration

NASS: National Agricultural Statistics Service

NCDC: National Climatic Data Center

NCEP: National Centers for Environmental Prediction

NDVI: Normalized Difference Vegetation Index

NDFD: National Digital Forecast Database

NH4+: Ammonium ion

NIR: Near Infrared

NOAA: National Oceanic and Atmospheric Administration

NO3-: Nitrate ion

NO_SD: without standard deviation

NMCGA: Noisy Monte Carlo Genetic Algorithm

P: Phosphorus

P: Precipitation

PM: Penman- Monteith

POWER: Prediction Of Worldwide Energy Resources

PT: Preistley-Taylor

R: Coefficient of correlation

R2: Coefficient of determinations

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R: Rainfall

RH: Relative humidity

RS: Remote Sensing

RSME: Root mean squared error

SD: Standard Deviation

SCF: Seasonal Climate Forecast

S2S: Sub seasonal to Seasonal

SEBAL: Surface Energy Balance Algorithm for Land

SEBS: Surface Energy Balance System

SAVI: Soil Adjusted Vegetation Index

SSEBop: Simplified Surface Energy Balance operational

SRGF: Soil Root Growth Factor

V: Vegetative stages of maize

VPD: Vapor Pressure Deficit

S-IDW: Stage based- Inverse Distance Weightage

SSURGO: Soil Survey Geographic Database

SWAP: Soil, Water, Atmosphere and Plant

T: Temperature

Tmax: Maximum temperature

Tmin: Minimum temperature

TIR: Thermal Infrared

TAW: Total available water

US: United States

USGS: United States Geological Survey

U2U: Useful to Usable

U.S.A: United States of America

WFO: Weather Forecast Offices

WMO: World Meteorological organization

WUE: Water Use Efficiency

WSS: Web Soil Survey

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1. INTRODUCTION

Maize (Zea mays L.) is a major crop grown in the Midwestern US. Most of the maize grown in Michigan are rainfed and used for biofuel and animal feeds. However, there are also irrigated system in the southwest region. To meet the growing demand on maize for feed, food and fuel, genetic performance of hybrid maize must be improved and agronomic practices, optimized.

Management decisions in maize are associated with critical crop growth stages and controlled by climatic variability. Moreover, these growth stages are closely related to crop productivity and has economic implications. Therefore, investigating phenological changes in maize associated with variable climate is important to improve management of resources and to achieve better yields.

With a goal of achieving high yields, plant breeders and agronomists have studied interactions of genotype, environment and management through field experiments (Elias et al.,

2016). However, field experiments are constrained by time and resources. Crop simulation models can facilitate the process by extrapolation of field experiments at larger spatiotemporal scales (Lobell et al., 2009). Crop simulation models have been used in assessing model performances and predicting yields (Rosenzweig et al., 2013). However, uncertainty in crop model output may arise due to the choice of the model (structure, parameter and assumption) or to erroneous input data (Zhang et al., 2019). Here, cultivar specific factors, so-called genetic coefficients, are important parameters used by the crop model for predicting crop daily growth and development response to weather, soil and management practices.

Estimation of crop genetic coefficients for specific agro climatic conditions is a first step to be done before a crop model can be applied (Wallach et al., 2001; Jones et al., 2011). This can be done through extensive field experiments or multiple methods, each has its own advantages and

1 limitations leading to uncertainty in parameter estimates, and hence prediction. A multi-model ensemble approach reduces the uncertainty and improves prediction. The strength of using an ensemble is that, it averages out biases, reduces the variance, and is unlikely to overfit. However, in the crop modelling community, there is a lack of consensus over ensembling parameters of the models instead of ensembling model itself (Challinor et al., 2009) unlike in hydrology field. To date, there is no documented study yet, which performs ensembling of crop genetic coefficients to simulate phenology and yield of maize (Zea mays L.).

Knowledge Gap 1: Lack of understanding on the applicability of ensemble genetic coefficients in a crop model. This dissertation will advance our understanding on the impact of ensembling of genetic coefficients in a maize model.

To address knowledge gap 1, the following objectives were developed:

(1) to calibrate genetic coefficients of a maize hybrid in Michigan using Genotype Coefficient

Calculator (GENCALC), Generalized Likelihood Uncertainty Estimation (GLUE), Noisy Monte

Carlo Genetic Algorithm (NMCGA), and an ensembling approach; and

(2) to validate estimated genetic coefficients in simulating maize phenology and yields in

Michigan.

In order to manage resources at a large spatiotemporal scale, remote sensing (RS) can play a significant role in observing and estimating change in biophysical parameters at the surface

(Lobell et al., 2018). Moreover, RS when integrated with ground measurements and crop simulation models can significantly improve management decisions. However, translating remote sensing data into field applications has its own limitations and uncertainties (Zhang et al.,

2016). Management decisions like irrigation during critical crop growth stages require accurate

2 estimation of evapotranspiration (ET). Traditionally, the standard FAO 56-crop coefficient (Kc) is used to estimate ET (as a product of Kc and reference ET (ETr) based on Penman-Monteith approach). However, the validity of these kc values may not be general in every agro-climatic condition. Therefore, region specific Kc-curves that reflect the genetic characteristics of a maize hybrid are needed to be estimated. The Kc values can be estimated (Kc = ETc/ETr) from in-situ methods or RS-based ET models. However, ET method/model choice and input data can lead to uncertainty and biases in the outputs. As with the genetic coefficient study above, a multi-model ensemble approach can improve estimates by reducing the uncertainty associated with individual models.

Knowledge Gap 2: Lack of utility of ensembled Evapotranspiration (ET) products in developing crop coefficients (Kc) using predicted growth stage for irrigation scheduling in maize.

There is a limited research in estimating crop coefficients from an ensemble of remote sensing ET (RS-ET) models and its application in irrigation scheduling of maize. This dissertation will advance our understanding on the applicability of crop coefficients developed from RS for irrigation scheduling of maize.

To address knowledge gap 2, the following objectives were developed:

(1) to estimate evapotranspiration (ET) using ensemble of models (RS-based SEBAL, Priestley-

Taylor-JPL, MODIS Penman-Monteith, and ground-based empirical model, FAO-kc);

(2) to derive crop coefficients of maize based on the ensembled ET and reference ET; and

(3) to validate derived kc-curves using a soil water balance model and design irrigation scheduling.

Moreover, management decisions in maize production are usually coordinated with specific growth stages, which are very sensitive to climate and weather variability (Egli, 2008). Rainfed maize in Michigan are sensitive to in-season climatic variability leading to year-to-year variability of phenological stages. This variability phenology entails risks and quantifying it will help in managing climate related risks. In maize, weather related disease risks are prevalent in the last few years. Due to continuous maize rotation with minimum tillage, conditions become more favourable for fungal diseases outbreaks (Wise and Mueller, 2011). In Michigan,

Gibberella and Fusarium, which produce mycotoxins, are the primarily causal organisms for the development of ear rot disease in maize (Chilvers, 2018). The pathogen spreads through infecting maize ears during early silk stage, approximately 6 to 8 days after silk emergence.

Ear rot disease infection coincides with the anthesis period. Hence, it is important to have an accurate prediction of this phenological stage in order to manage this disease. The calibrated crop model can be used to predict anthesis using seasonal climate forecast (SCF) (Apipattanavis et al., 2010). However, climate forecasts produced by NOAA are in tercile probabilities, which are needed to be downscaled on a daily basis if they are to be linked with a crop model. While methods exist for downscaling rainfall probabilities (Han et al., 2017), downscaling temperature probabilities from SCF are not readily available. Temperature is critical for predicting phenology. Growing Degree Days (GDD), a temperature-based index of thermal time, is frequently used to estimate crop growth and development rates. The U2U GDD Tool predicts silking and maturity dates based on GDD and is used by Midwest maize growers (Prokopy et al.,

2017). It provides a projection of GDD 30 days in advance at the seasonal horizon (3 months).

However, the tool only uses historical and forecast temperature (30- days). Moreover, it does not

4 use seasonal climate forecast (3-month) and hence cannot predict silking with a lead-time of more than one month.

Knowledge Gap 3: Lack of downscaling tool for seasonal rainfall and temperature forecast to predict anthesis and risk assessment model to manage ear rot disease in maize.

To address knowledge gap 3, the following objectives were developed:

(1) to predict phenology using the calibrated CERES-Maize and downscaled seasonal climate forecast using FResamplePT tool; and

(2) to develop and validate the risk analysis model for ear rot disease management in maize using seasonal climate forecast.

This dissertation will integrate seasonal climate predictions, remote sensing and crop models to address, advance our understanding and bridge lack of the three knowledge gaps mentioned above.

2. INTRODUCTION TO METHODOLOGY AND RESULTS

This dissertation is a compendium of three research studies that are co-dependent to address some pressing issues on the agronomic management of corn in Michigan under irrigated and rainfed conditions. The general methodology revolves around the prediction of phenology using a calibrated crop model (CERES-Maize) (Fig. 2.1). The calibrated crop model in the first study was used to develop region specific crop coefficients in the second study for improving irrigation management in corn. Innovative evapotranspiration (ET) estimation and ensembling techniques were used and developed in the process. In the third study, the calibrated crop model was used to predict anthesis using seasonal climate forecasts (SCF). Predicted anthesis from the calibrated crop model was used to drilldown on the critical period for identifying ear rot disease risks.

The first study entitled “Estimating genetic coefficients of CERES-Maize to simulate phenology and yield of maize in Michigan” presented methods of estimating genetic coefficients of CERES- Maize and evaluated their performance in simulating phenology and yields. The calibration methods include GENCALC, GLUE and NMCGA. More robust crop genetic coefficients were estimated based on ensembling the parameters derived from the individual methods. Validation of the calibrated genetic coefficients however revealed that soil model structure under rainfed/stressed conditions must be adjusted to allow the root system of hybrid maize to explore available water and nutrient in the soil profile.

Figure 2. 1. The general flowchart for the methodology of this dissertation.

The second study entitled “Estimation and validation of remotely sensed evapotranspiration for the development of crop coefficients of maize and irrigation scheduling” evaluated multiple evapotranspiration (ET) models from satellite-based model (SEBAL,

Priestley-Taylor-JPL and MODIS Penman - Monteith) to ground-based empirical model (FAO- kc) using eddy-covariance flux towers. The calibrated CERES-Maize in the first study was used to simulate crop growth stages, which were used as bases of ensembling ET from different methods using a stage-based Inverse Distance Weightage (IDW) approach. Ensembled ET was used to derive region specific crop coefficients for maize irrigation scheduling in Southwest

Michigan.

Lastly, the third study entitled “Downscaling seasonal rainfall and temperature forecast to

7 develop risk analysis model for ear rot disease management in maize” used tercile-based seasonal climate forecasts (SCF, rainfall and temperature) from NOAA and calibrated CERES-

Maize model to predict anthesis to inform when would be the critical period for evaluating ear rot risks in maize. An innovative SCF downscaling technique for rainfall (P) and temperature (T) was introduced, which was used to develop risk analysis model for ear rot disease management in maize. The risk analysis model showed an enhanced ear rot risks in 2018 relative to climatological risks.

3. ESTIMATING GENETIC COEFFICIENTS OF CERES-MAIZE TO SIMULATE

PHENOLOGY AND YIELD OF MAIZE IN MICHIGAN

3.1 Introduction

Maize (Zea mays L.) is a major crop grown in the Midwestern US. The economic incentives associated with the ethanol mandate and the biotechnological revolution in developing transgenic crops encouraged the expansion of maize acreage (Fausti, 2015). Michigan maize farmers harvested a total production of 6.5 million tons with 10 tons ha-1 average yield on 6.63% of the state’s land compared to 11 tons ha-1 national average yield. (USDA-NASS, 2017). Most of the maize grown in the state is rainfed and used for biofuel and animal feeds. However, the lower part of southwest Michigan, having fertile and sandy soils, grow hybrid seed under intensive irrigation. To meet the growing demand, the genetic performance of hybrid maize must be improved and agronomic practices, optimized.

Varietal improvements and development however require a lot of time and resources.

Since the mid-20th century, yield increments in maize have been attributed ~50-60% to improved genetics, and ~40-50 % to management practices (Duvick, 2005; Kucharik and Ramankutty,

2005; Lee and Tollenaar, 2007; Egli, 2008; and Sacks and Kucharik, 2011). The impacts of climate change like, longer growing period and more summer rainfall in the last few decades, benefited maize production through greater accumulation of photosynthates (Andresen et al.,

2001, Lobell and Asner, 2003, Twine and Kucharik, 2009). Plant breeders account for these physiological gains due to climate change in the process of developing modern hybrids. Prior to the release of advanced cultivars, these are grown over several years at various locations to evaluate the Genotype x Environment interactions (G x E; Elias et al., 2016; Kleinknecht et al.,

2016; Paderewski et al., 2016; van Eeuwijk et al., 2016 and Yan, 2016). Although plant breeders

9 have leveraged these interactions, within seasonal variability of weather plays a challenging role.

Sound agronomic management within the season can help overcome these challenges and minimize yield gap (i.e., difference of potential and actual yields). However, improving management and cultivar development by traditional agronomic research methods are constrained by time (Vilayvong et al., 2015). Crop simulation models can facilitate the cultivar development process by virtual extrapolations of field experiments at multiple locations and seasons (Lobell et al., 2009).

Crop simulation models have been used in studying growth and development of crops under different environments (White and Hoogenboom, 2010; Asseng et al., 2013; MacCarthy et al., 2017 and Jha et al., 2018). Crop growth models integrate knowledge in soil science, plant physiology, micrometeorology and agronomy to simulate crop performance (van Ittersum et al.,

2003; Yin et al., 2004; Löffler et al., 2005 and Cooper et al., 2009). Crop cultivar specific traits control the interactions of environmental factors (e.g., temperature, daylength, light) and plant processes (growth and development). These crop model parameters are called “genetic coefficients” and they describe specific growth and development characteristics of a crop cultivar

(White and Hoogenboom, 1996; Boote et al., 2003; Hoogenboom et al., 2004). The first step in the applications of crop models is the estimation of these genetic coefficients (Wallach et al.,

2001; Jones et al., 2011). They can be estimated by extensive and exhaustive field and laboratory experiments (Du Toit, 2002; Suriharn et al., 2007). However, this process can be hastened and simplified by using carefully designed model calibration and validation, and was the focus of this study.

The Decision Support System for Agro-technology Transfer (DSSAT) is a globally accepted decision support tool. (Tsuji, 1998; Jones et al., 2003 and Hoogenboom et al., 2013).

The Cropping System Model (CSM) in DSSAT is a suite of models, which incorporates more than 40 crop models, integrates soil, weather and management for simulating growth and development of crops (Hoogenboom et al., 2013). The CERES-Maize (Jones and Kiniry, 1986) in CSM is the most widely used maize model for the assessment of management practices on maize production. Due to advances in crop physiology, climatology and agronomy, CERES-

Maize has evolved over time. It was adopted in APSIM (Keating et al., 2003) by revising the soil organic pool. Lizaso et al. (2011) have developed CSM-IXIM by revising codes of CERES-

Maize model for carbon assimilation and partitioning. CERES-Maize has also been used to study nitrogen cycle in long term maize production (Liu et al., 2011; Li et al., 2015). CERES-Maize cultivar parameters have been calibrated to optimize irrigation practices in Pakistan (Mubeen et al., 2016), to evaluate management practices in Ghana and Tanzania (Mourice et al., 2014;

MacCarthy et al., 2017), to evaluate climate resilient technologies in Bangladesh (Ahmed et al.,

2017), and to design management strategies under data-scarce environments in Nigeria (Adnan et al., 2019), among others.

Several approaches have been adopted to estimate crop model parameters. Grimm et al.

(1993) used downhill simplex to estimate phenological parameters of soybean cultivars.

Simulated annealing was used to estimate soil and root parameters of a soybean model (Calomn et al., 1999; Mavromatis et al., 2002). Hunt et al. (1993) developed GENCALC (Genotype

Coefficient Calculator) based on sequential search method. GENCALC estimates the coefficients by multiple iterations of approximate coefficients in a pre-set sequence and compares outputs based on the difference between simulated and observed values (e.g., anthesis and maturity dates, yields). The genotype coefficients are altered until a good model fit is found. (Hunt et al.,

1993). The genetic coefficients of groundnut (Arachis hypogea L.) (Anothai et al., 2008),

11 soybean (Glycine max L.) (Bao et al., 2015), wheat (Triticum aestivum L.) (Ibrahim et al., 2016) and maize (Román-Paoli et al., 2000; Hassanien et al., 2007; Yang et al., 2009; Bao et al., 2017 and Adnan et al., 2019) have been estimated using GENCALC. However, GENCALC does not estimate uncertainties of the derived parameters (He et al., 2010). A detailed review of methods for calibrating model parameters, and discussions about the past, present and future of model calibrations can be found in Siedel et al. (2018).

Model outputs are prone to errors due to uncertainties in data inputs and model parameters. It is unrealistic to conclude that one set of parameters represents the model behaviour; rather it is better to assess likelihood weights of the parameters, which can better predict the model behaviour (Beven and Binley, 1992). A Bayesian framework that assesses uncertainty of parameters using Monte Carlo technique, called Generalized Likelihood

Uncertainty Estimation (GLUE) overcomes GENCALC’s limitation (Campbell et al., 1999;

Mertens et al., 2004; Candela et al., 2005; He et al., 2009). It uses observed data to develop prior parameter distributions (He, 2008). The posterior distribution is computed based on Bayes’ theorem (Makowski et al., 2006). GLUE has been used in the field of hydrology and crop sciences for parameter estimation (e.g., Yan et al., 2017; Beven, 2018; Sikorska and Seibert,

2018; He et al., 2009; He et al., 2010; López-Cruz, 2016; Sun et al., 2016). Several works have compared GENCALC and GLUE in wheat, rice and maize calibrations but did not find any significant differences in performance between the methods (e.g., Ibrahim et al., 2016;

Budhhaboon et al., 2018; Adnan et al., 2019).

Genetic Algorithm (GA; Goldberg, 1989) also has been used in crop model calibration, sometimes outperforming other gradients or Bayesian-based optimization methods because of its ability to search through vast search spaces (Miller and Goldberg, 1996; Smalley et al., 2000;

Gopalakrishnan et al., 2003; Wu et al., 2006). Pabico et al. (1999) used GA to determine genetic coefficients of cultivars, mapping coefficients as a single chromosome. The Noisy Monte Carlo

Genetic Algorithm (NMCGA; Ines and Mohanty, 2008a) evaluates realizations of strings

(analogous to chromosome) of model parameters with given distributions using Monte Carlo approach (Wang, 1991; Ines and Droogers, 2002; Pabico, 2007; Ines and Mohanty, 2008b; Dai et al., 2009). In each generation, fitter chromosomes (least difference between observed and predicted) are selected; undergo crossover and mutation until a solution is achieved. Shin et al.

(2013) used NMCGA to estimate soil hydraulic parameters of an agro-hydrological model,

SWAP (Van Dam., 2000).

Estimating parameters using individual methods always possess some levels of uncertainty due to errors in initial conditions, data and model structure. Ensemble-based parameter estimation methods can improve accuracy and account for different sources of uncertainties in model calibration (Chen et al., 2015) and provide more robust estimates of model parameters (Vrugt and Robinson, 2007). Parameters estimated by one method can better predict phenology, others can better predict growth. Ensembling parameters derived from different methods can account for the biases associated with the methods, and can better predict phenology and yields (Jha et al., 2019, in review). In order to achieve robust genetic coefficients, which can simulate phenology and yield of maize in Michigan, we designed this study to ensemble genetic coefficients from multiple methods and evaluate the model performance.

The specific objectives of the study are: (i) to calibrate genetic coefficients of a maize hybrid in Michigan using multiple methods, and (ii) to validate CERES-Maize model for simulating maize hybrid phenology and yields in Michigan. In this study, we used three methods for estimating genetic coefficients of CERES-Maize namely, GENCALC, GLUE and NMCGA,

13 employed an ensembling approach, and assessed their performance. We used maize performance trial locations in Michigan in the calibration and validation. Irrigated locations were used for calibration and rainfed locations for validation.

3.2 Materials and Methods

3.2.1 Study area

Data for this study were collected from the field experiments conducted at Michigan

Maize Performance Trial locations (MMPT, Singh et al., 2018). Performances of commercial maize hybrids are evaluated at MMPT locations annually, which lies in different zones ranging from south to north across Michigan (Fig. 3.1). Climatic conditions are similar within each zone and consisted of three trial locations in each zone.

Figure 3. 1. Locations of maize performance trials in Michigan.

These zones were established based on long-term accumulated growing degree-days

(GDD). The 30-year (1981-2010) normal accumulated GDDF from May 1 to October 31 were

2557 oF (1421 ℃), 2478 oF (1377 ℃), and 2342 oF (1301 ℃) for zone 1, 2, and 3, respectively.

GDD for maize growth and development are calculated by deducting the base temperature for maize growth (50 ᵒF/10 ᵒC) (Cross and Zuber, 1972; Stewart et al., 1998) from the average air

14 temperature in a 24-hour period, starting from the emergence date (Abendroth et al., 2010; Angel et al., 2017). The upper threshold for optimum growth in maize is considered (86 ᵒF/30 ᵒC) for calculating average temperature. It means that whenever air temperature goes beyond 86 ᵒF/30

ᵒC, the daily maximum temperature has to be set equal to 86 ᵒF/30 ᵒC.

Based on heat accumulation, seed companies provide information of relative maturity to understand the crop maturity period (planting to physiological maturity) with specified GDD numbers from planting to anthesis and to maturity, respectively. The GDD estimates are based on ᵒF for the hybrids in the US and for this study. However, genetic coefficients which controls growth and development are based on ᵒC. GDDF can be converted into GDDC by dividing the final value of GDDF by 1.8 (Abendroth et al., 2010). The zones, coordinates, major soil types and management practices of the field trials used in this study are given in Table 3.1.

Table 3. 1. Location of the maize fields under Michigan Maize Performance Trials (MMPT). Name of County Zone Coordinates Major Soil Typea Management Practice (Town) (Rainfed/Irrigated) Washtenaw 1 42.15 N, 83.56 W Sandy Loam Rainfed (Milan) Branch 1 41.97 N, 85.08 W Sandy Loam Irrigated (Coldwater) Cass 1 41.86 N, 85.88 W Loam Irrigated (Vandalia) Allegan 2 42.57 N, 85.63 W Loam Rainfed (Martin) Ingham 2 42.71 N, 84.47 W Loam Rainfed (Williamston) Saginaw 2 43.13 N, 83.97 W Loam Rainfed (New Lothrop) Huron 3 43.83 N, 82.98 W Sandy Loam Rainfed (Bad Axe) Montcalm 3 43.22 N, 85.21 W Sandy Loam Rainfed (Greenville) Mason 3 43.98 N, 86.15 W Loam Irrigated (Scottville) a Source: WSS-SSURGO Database.

3.2.2 CERES-Maize model

CERES-Maize is a crop module embedded within the suite of Cropping System Model

(CSM) in DSSAT (Jones and Kiniry, 1986; Boote et al., 2010; Hoogenboom et al., 2013).

DSSAT facilitates the assessment and evaluation of different management practices on growth and development of crops with a goal of enhancing current knowledge of Genotype X

Environment X Management interactions (Boote et al., 2010; Elias et al., 2016). CERES-Maize, a Fortran based process-oriented model, utilizes daily weather data to simulate crop growth stages on a daily basis, integrating soil water and nitrogen balance associated with maize growth.

Therefore, CSM in DSSAT integrates the interaction and effects of climate, soil and other management practices, which can be used to predict/assess their impacts on crop growth and development in the past, present and future (Lobell et al., 2009).

Based on heat accumulation and photoperiod, the model assumes that the rate of development increases linearly above a base temperature (10ᵒ C) until 34ᵒC and decreases linearly from 34 to 44ᵒC, which are governed by genetic coefficients (P1, P2, P5, PHINT; Table 3.3). The phenological development also accounts for the process of morphological development of leaves, stems and roots resulting in biomass accumulation and partitioning. CERES-Maize growth progresses through phenological stages. The simulated growth stages are sequential, starting from emergence to end of juvenile phase, followed by peak vegetative growth, which culminates at tassel initiation. There is a small transition phase from vegetative to reproductive stage, which starts from tassel initiation and ends at anthesis. The last two stages in the crop season are anthesis to start of grain filling, followed by effective grain filling period. These stages coincide with high water and nutrient demands, which translate biomass partitioning into grain filling.

The phenological stages are governed by genetic coefficients (P’s) that depends mostly on temperature and daylength (Kiniry and Bonhomme, 1991).

3.2.3 Input data for CERES-Maize

3.2.3.1. Weather data

Weather data were collected from MSU Enviro-weather Network, a weather-based information system that helps growers and stakeholders in making farm related decisions in

Michigan. Daily weather data (rainfall, minimum and maximum temperature, solar radiation) for the 2017 and 2018 cropping seasons (May-October) are shown in Fig. 3.2 and 3.3 respectively.

All the weather data were ingested in the weather database of DSSAT using WeatherMan, which converts data to DSSAT weather format (Pickering et al., 1994). Average seasonal values of total rainfall, solar radiation, maximum and minimum temperatures are given in Table 3.2. Except for

Huron, all the stations had greater total solar radiation in 2017 growing season than 2018, which could have benefited the crops to better accumulate their potential biomass productions in 2017.

The rainfall observation also suggests that 2018 growing season was wetter than 2017.

Table 3. 2. Summary of weather data (2017 and 2018) during the crop season (May-October) for selected locations. Locations Zones Average Solar Average Average Total Radiation Tmax (ᵒC) Tmin (ᵒC) Rainfall (mm) (MJ m-2 day-1) 2017 2018 2017 2018 2017 2018 2017 2018 Washtenaw 1 25.04 25.51 11.86 13.03 360.4 406.0 18.03 16.72 Branch 1 23.39 25.35 9.82 13.23 428.2 573.6 16.36 14.25 Cass 1 23.92 24.80 12.40 13.90 585.8 572.2 16.47 14.99 Allegan 2 24.20 24.72 11.67 13.20 593.6 679.7 18.69 17.03 Ingham 2 23.45 23.93 11.48 12.11 389.2 464.8 16.96 15.78 Saginaw 2 24.01 24.62 12.20 13.15 466.9 493.1 17.54 17.09 Huron 3 22.76 23.27 11.53 11.81 477.4 348.7 16.36 16.67 Montcalm 3 23.18 23.59 10.35 11.37 485.5 656.6 16.22 15.63 Mason 3 21.94 22.59 10.72 11.40 528.6 539.4 18.38 17.68

3.2.3.2 Soil data and agronomic management

All nine locations were categorized under two major soil types i.e., loam and sandy loam, based on major soil type according to WSS-SSURGO database (Table 3.1). Initial conditions for sandy loam soil (volumetric water = 0.22 cm3/cm3; Soil-N (NH4+) = 3 g(N) / Mg (Soil) and Soil-

N (NO3-) = 5 g(N) / Mg (Soil)) and for loam soil (volumetric water = 0.40 cm3/cm3; Soil-N

(NH4+) = 5 g(N) / Mg (Soil) and Soil-N (NO3-) = 6 g(N) / Mg (Soil)) were kept in the medium range (Rutan and Steinke, 2017). Field data like fertilizer applications, planting and anthesis

(75% of silking) dates and yields are given in Table 3.3. However, due to limited data availability on maturity (black layer), dates they were estimated based on degree-days accumulation for 2500 GDDF using U2U tool (Angel et al., 2017) for 2017 and 2018 for all locations (Table 3.1). The split doses of nitrogen were applied uniformly at each location, first

25% of N (as Urea) was applied during planting and 75% of remaining N was applied 30-40 days after planting as urea ammonium nitrate solution during V6 to V8 stages. Farm Yard

Manure (FYM) was applied one week before planting at Allegan, Ingham, Huron and Mason sites. The harvested yields were estimated from the centre two rows from the plots size of four rows with 6.7 m length and a row spacing of 0.76 m. Those rows were harvested with a Kincaid

8-XP plot combine after physiological maturity to collect data on grain yield and moisture content. The final yields were estimated on dry basis (adjusted to 0% moisture).

Table 3. 3. Descriptions of field observations used in the study. Name of Year Fertilizer & Manure Planting Anthesis Maturity* Yield Location Application Date (DAP) (DAP) (Kg.ha- (N-P-K; Kg.ha-1) 1) Washtenaw 2017 206-10-3 15-May 72 132 10984 2018 206-10-3 12-June 60 120 9723 Branch 2017 248-10-3 29-May 72 139 12477 2018 213-10-3 29-May 66 116 8698 Cass 2017 269-10-3 14-May 76 138 12688 2018 274-10-3 09-May 67 129 9041 Allegan 2017 122-10-3 + 20 ton FYM 12-May 70 130 13391 2018 179-10-3 + 10 ton FYM 18-May 62 114 9769 Ingham 2017 181-10-3 + 10 ton FYM 18-May 69 138 11010 2018 179-10-3 + 10 ton FYM 08-May 71 123 8197 Saginaw 2017 172-10-3 29-May 66 131 10451 2018 179-10-3 30-May 65 116 9297 Huron 2017 142-10-3 + 15 tons FYM 17-May 78 145 11034 2018 179-10-3 + 10 tons FYM 16-May 72 128 8245 Montcalm 2017 172-10-3 16-May 79 155 10820 2018 179-10-3 18-May 64 124 8973 Mason 2017 122-10-3 + 20 tons FYM 10-May 73 141 13578 2018 179-10-3 + 10 tons FYM 23-May 70 147 8951 *Estimated from U2U tool (https://mrcc.illinois.edu/U2U/gdd/); FYM- Farm Yard Manure

Figure 3. 2. Weather data (Rainfall, solar radiation, maximum and minimum temperature)for cropping season (May-October) for 2017 (Source: MSU Enviroweather)

Figure 3. 3. Weather data (Rainfall, solar radiation, maximum and minimum temperature) for cropping season (May-October) for 2018. (Source: MSU Enviroweather)

3.2.4 Calibration methods

3.2.4.1. Genetic coefficients

Maize hybrid of Comparative Relative Maturity (CRM) group 103 (Lauer, 1998) that requires around 2500 GDD from planting to physiological maturity was selected for the calibration. However, these specifications of GDD requirements for physiological maturity changes with different hybrid seed companies. Calibrations were conducted using all three irrigated locations (Branch, Cass and Mason) out of total nine varietal trial locations during 2017 and 2018 growing seasons. Maize grown at irrigated locations were selected in the calibration, as the best conditions (water and nutrients) are required for the calibration process (Lobell et al.,

2009). The starting cultivar selected for GENCALC was PC0001 (2500-2600 GDD) from the

DSSAT database (DSSAT V4.6: Hoogenboom et al., 2013), which is suitable for Michigan based on GDD accumulation.

The descriptions of genetic coefficients in CERES-Maize are listed in Table 3.4. P1 is the thermal time needed for phase change from germination to end of juvenile, when photoperiod does not have any significant role in phasic development, expressed in degree-days (Jones and

Kiniry, 1986). P2 is expressed in days for delay when photoperiod is increased each hour above the longest photoperiod (12.5 hr) having maximum development rate (Hanks and Ritchie, 1991).

Tassel initiation is controlled by both P1 and P2 (Román-Paoli et al., 2000). Tassel initiation starts approximately 4 days after P1 completion (Jones and Kiniry, 1986). P5 represents thermal time from anthesis to physiological maturity and expressed in degree-days. G2 is the maximum number of kernel a plant can grow potentially. Du Toit (2002) explained how fitted coefficients

(estimated) have differences in model predictions compared with determined coefficients from the field after rigorous multi-year experiments. G3 controls the kernel-filling rate during post

21 anthesis to maturity phase and expressed in mg day-1. P1 and P2 determine anthesis, and P2 and

P5 determine maturity dates, while P5, G2, G3 and PHINT control the yield and its components e.g., dry matter, grain size and canopy weight. PHINT controls phenology and growth as well, through determination of the timing to leaf tip appearance (Hammad et al., 2018). PHINT was kept fixed in the calibration, while the other five parameters described above were calibrated. It is recommended not to change PHINT unless sufficient field data for leaf numbers are available

(DSSAT v 4.6; Hoogenboom et al., 2013).

These parameters determine the phasic development of a maize cultivar, and these developments are attributed to genetic variations among the cultivars, hence called genetic coefficients.

Table 3. 4. Descriptions of genetic coefficients in CERES-Maize. Genetic coefficient Description PC0001 (2500-2600 GDD) (Starting cultivar for GENCALC) Phenology Coefficient Pl Juvenile phase coefficient, °C-d 168.9 P2 Photoperiod sensitivity coefficient, days 0.734 P5 Grain-filling duration coefficient, °C-d 780.0 Growth Coefficient G2 Potential kernel number coefficient 750.0 G3 Kernel filling rate, mg/day 8.50 PHINT Phylochron interval, °C-d 32.33 (fixed in calibration) 3.2.4.2. GENCALC: Genetic Calculator

GENCALC is a built-in software in DSSAT, which estimates genetic coefficients using a gradient search method (Fig. 3.4; Hunt et al., 1993; Adman, 2019). With the pre-defined set of experiments and starting cultivar coefficients of a selected maize variety, it runs CERES-Maize iteratively to search for the best parameter estimates. With an initial value for each parameter, it adjusts genetic coefficients until it fits to the provided observed value within the range of their

22 physiological characteristics i.e., flowering date, maturity date, daylenth, kernel size, etc. (Hunt et al., 1993). The algorithm in the software exploits a known point in the search space that depends on a starting point (i.e., coefficients of starting cultivar) and based on the differences between simulated and observed values, it adjust the coefficients.

Figure 3. 4. GENCALC-CERES-Maize working flowchart (Adnan et al., 2019).

It minimizes the error between simulated and observed values in each run (Hunt et al.,

1993). Because of a small sampling area of the search space, the final coefficients cannot be optimized for large ranges of physiological characteristics (Pabico et al., 1999). Pre-defined

“Targets” (a measured crop traits) and “Rules” (which govern the sequence of genotype coefficient calculation), govern the search until the best fit to each observation is found. After multiple iterations, the parameter set that gives the best fit with observed anthesis date (ADAP), maturity date (MDAP) and yield (HWAM) is chosen. GENCALC estimates genetic coefficients

23 in two steps, optimizing the phenology parameters (P1, P2, and P5) first, and then the growth parameters (G2, G3). The phenological development depends on degree-day accumulation and growth depends on phenology (Fig. 3.4). Hence, it is logical that phenology parameter is derived first and then growth parameters. GENCALC does not estimate uncertainties of parameters.

3.2.4.3. Generalized Likelihood Uncertainty Estimate: GLUE

GLUE estimates parameters using a Bayesian approach. GLUE first develops the prior parameter distributions using genetic coefficients from the DSSAT database (Hoogenboom et al.,

2013) by fitting them to a multivariate normal distribution, and then estimates the posterior distributions of each parameter using Bayes’ theorem (Eq. 1);

푃(푂|휃)푃(휃) 푃(휃|푂) = , (1) 푃(푂) where θ and O represent the parameter set and observations, respectively. P (θ|O) is the posterior distribution. P (O|θ) is the likelihood, P(θ) is the prior probability and P(O) is a normalizing constant.

To calculate likelihood values, random parameter sets θi are generated from the prior distributions. The more the number of parameters set realizations, the more stable results can be obtained (He et al., 2010). For stability in results, we selected 30,000 runs for GLUE. A likelihood value L [θi|O] for each observation (anthesis date, maturity date and yield) is estimated based on Gaussian likelihood function (Eq. 2) (He et al., 2010).

2 푀 1 [푂푗−푌(휃푖 )] 퐿[휃푖|푂] = ∏푗=1 2 푒푥푝 {− 2 } (2) √(2휋휎표) 2휎표

th th 2 where θi is the i parameter set, M is the number of observations, Oj is the j observation, σo is the variance of model error and Y(θi) is the output of the model. In addition, Eq. 3 calculates the probability of the parameter set,

퐿(휃푖|푂) 푝(휃푖) = 푁 (3) ∑푗=1 퐿 (휃푖|푂)

th where, p(θi) is the probability or likelihood weight of the i parameter set θi, L(θi|O) is the likelihood value of parameter set θi, given observations O (He et al., 2010)

The empirical posterior distributions were constructed from the pairs of parameter set and probabilities (θi, p (θi), i=1,…,N). The means and variances of those chosen parameters were calculated as in Eqs. 4, 5 (He et al., 2010);

푁 휇푝표푠푡(휃) = ∑푖=1 푝 (휃푖) ∗ 휃푖 (4)

2 푁 2 휎푝표푠푡(휃) = ∑푖=1 푝(휃푖) ∗ (휃푖 − 휇푝표푠푡(휃)) (5)

2 where µpost (θ) and σ post(θ) are the mean and variance of the posterior distribution of parameters

th θ; and p(θi) is the probability of the i parameter set.

The parameter estimation in GLUE follows a similar step as in GENCALC i.e., estimate first the phenology parameters (P1, P2, P5) and then the growth parameters (G2, G3). The parameter set that gives the maximum likelihood value is selected.

3.2.4.4. Noisy Monte Carlo Genetic Algorithm: NMCGA

There are multiple methods of parameter estimation, each has its own advantages and disadvantages. Ensemble-based parameter estimation methods tend to improve model accuracy and accounts for different sources of uncertainty, thus provide more robust estimates of model parameters (Vrugt and Robinson, 2007). Along with GENCALC and GLUE, we also employed the Noisy Monte Carlo Genetic Algorithm (NMCGA; Ines and Mohanty, 2008a) to estimate maize genetic coefficients. Here, genetic algorithm (GA) estimates combination of parameters

(i.e., means and standard deviations) and evaluate their fitness. Based on a-priori distributions and a-priori range of parameter values from DSSAT cultivar database, parameter set are

25 evaluated using Monte Carlo resampling. Resampled parameters sets are then passed to CERES-

Maize to evaluate the fitness of that parameter set.

The fitness of the parameters are tested by evaluating the difference between simulated and observed values. NMCGA, being a noisy GA, evaluates the fitness of a parameter set under a noisy fitness space (Wu et al., 2006), thus an overall fitness of a parameter set is evaluated from the average fitness of several ensemble runs from parameter set realizations. The fittest parameters are selected and allowed to reproduce for multiple generations undergoing crossovers and mutations until an optimal solution is achieved. For consistency, we also employed a two- step parameter estimation technique like in GENCALC and GLUE i.e., estimating phenology parameters first, then growth parameters. The coefficients (first and second moments) of phenology (P1, P2 and P5) and growth (G2 and G3) were arranged as a set of genes in a chromosome during those steps, respectively.

The objective function of the parameter set for the ith ensemble is formulated as Eq. 6;

1 푇 1 푁푟푒푠푎푚푝푙푒 푟 푂푏푗(퐾)푖 = 푀푖푛 ( ∑푡=1 | (∑ 푆푖푚 (퐾 )푡푖) − 푂푏푠푡|) ∀푖 (6) 푇 푁푟푒푠푎푚푝푙푒 푟=1 where, Kr is set of K parameters combinations with r realizations generated from Monte Carlo

r resampling and Nresample is the total number of realizations for simulated (Sim (K )) and observed variables (Obst), ti is running index for time T (Ines and Mohanty, 2008a). Noisy fitness is calculated using the inverse of the modified-penalty approach of Hilton and Culver (2000) (Eqs.

7, 8);

푍(퐾)푖 = 푂푏푗(퐾)푖 (1 + 푃푒푛푎푙푡푦 (퐾)푖) ∀푖 (7)

∗ 1 푓푖푡푛푒푠푠(푝 )푖 = ∀푖 (8) 푍(퐾)푖 where, p* is the chromosome, and fitness(p*) is the noisy fitness of that chromosome sampled from each ensemble i of the Monte Carlo resampling. A chromosome realization is penalized

(Penalty (K)) if its predicted variables violate some preset rules against the goodness-of-fit evaluation (Ines and Mohanty, 2008a). Sampling fitness is calculated based on Eq. 9 to reduce the noise in fitness;

1 푆푓푖푡푛푒푠푠 (푝∗) = ∑푅 푓푖푡푛푒푠푠(푝∗) (9) 푅 푖=1 푖 where, R is total number of ensemble i. The arrays of parameters set (chromosome) of means and standard deviations, undergo through the search process until the best chromosome is generated.

When calibrating for phenology, ADAP and MDAP were given the same weights while

HWAM was not used in the objective function (Eq. 6). When calibrating for growth, HWAM was used in the objective function while ADAP and MDAP were not. Moreover, we ran

NMCGA in two ways, one by estimating only the means of parameters (NMCGA_NO_SD), and other by estimating both the means and standard deviations of the parameters (NMCGA_SD).

The representations of p* as used in this study are given in Table 3.5.

Table 3. 5. Representations of phenology and growth parameters in NMCGA. NMCGA_SD NMCGA_NO_SD Paramet Minimu Maximu Number Minimu Maximu Number er m m of Bits 2L m m of Bits 2L Values Values (L) Values Values (L) Genetic Algorithm Variables µ(P1) 5 458 8 25 110 458 8 25 6 6 σ(P1) 0 67.56 8 25 0 0 2 4 6 µ(P2) 0 2 8 25 0 2 8 25 6 6 σ(P2) 0 0.28 8 25 0 0 2 4 6 µ(P5) 390 1035 8 25 390 1035 8 25 6 6 σ(P5) 0 117.85 8 25 0 0 2 4 6 µ(G2) 248 1170 8 25 248 1170 8 25 6 6 σ(G2) 0 161.63 8 25 0 0 2 4 6 µ(G3) 4.8 16.5 8 25 4.8 16.5 8 25 6 6 σ(G3) 0 1.76 8 25 0 0 2 4 6 Monte Carlo Variables Pl 110 458 110 458 P2 0 2 0 2 P5 390 1035 390 1035 G2 248 1170 248 1170 G3 4.8 16.5 4.8 16.5

3.2.5. Ensembling approach

Along with the comparisons of GENCALC, GLUE and two variants of NMCGA, we evaluated an ensembling approach of estimating crop model parameters. The purpose of ensembling was to integrate the strengths of the four methods with a goal of achieving a more robust set of genetic coefficients. The general framework is shown in Eqs. 10-12;

푁 푃푗 = ∑푖=1 푊푖 푝푖푗 , ∀푗 (10)

푤푖 푊푖 = 푁 , ∀푖 (11) ∑푖=1 푤푖

퐾 1 푤푖 = ∑푘=1 훽푘 2 , ∀푖, ∀푗 (12) (퐴푝푖푗,푘−퐴표,푘) where Pj is the ensembled value of a phenology or growth parameter, Wi is the weight of a parameter from method i, pij is the parameter value of a phenology or growth parameter from method i, j is an index of a phenology or growth parameter. wi is the inverse squared-distance between the predicted (Apij,k) and observed (Ao,k) variable(s) (e.g., ADAP, MDAP or HWAM) substantially impacted by that phenology or growth parameter. N is the number of methods

(here, N=4), k is an index for predicted or observed variable(s), K is the number of variable(s) impacted by pij and 훽푘 is the weight for that variable, k ( Jha et al., 2019, in review).

However, since HWAM and MDAP have different units, Eq. 12 is transformed to Eq. 13.

1 푤 = ∑퐾 훽 , ∀푖, ∀푗 (13) 푖 푘=1 푘 퐴푝푖푗,푘−퐴표,푘 ( )2 퐴표,푘

For arithmetic average, Wi = 1/N for all i’s.

3.2.6 Validation and statistical analysis

Before validation, the calibrated model was used to estimate potential productions (no water and nutrient stresses) for all locations to analyze the genetic potentials of the calibrated cultivar in those locations. All rainfed locations (Washtenaw, Allegan, Ingham, Saginaw, Huron and Montcalm) were used for validation (Table 3.1). Genetic coefficients derived from

GENCALC, GLUE, two variants of NMCGA, and the ensembling approach were used to validate CERES-Maize under rainfed conditions. Validations were done for both growing seasons (2017 and 2018).

We compared predicted and observed ADAP, MDAP and HWAM and used the coefficient of determination (R2; Eq. 14), Mean Bias Error (MBE; Eq. 15), Root Mean Square

Error (RMSE; Eq. 16) and Index of Agreement (d-index; Eq. 17) (Willmott, 1982) to measure the performances of the calibration methods,

푛 ̅ ̅ 2 2 [∑푖=0(푂−푂)(푀−푀)] 푅 = 푛 ̅ 2 푛 ̅ 2 (14) ∑푖=0(푂−푂) ∑푖=0(푀−푀)

1 푀퐵퐸 = ∑푛(푀 − 푂) (15) 푛 1

∑푛(푀−푂)2 푅표표푡 푀푒푎푛 푆푞푢푎푟푒 퐸푟푟표푟 = √ 1 (16) 푛

∑푛(푂−푀)2 퐼푛푑푒푥 표푓 퐴푔푟푒푒푚푒푛푡 (푑 − 푖푛푑푒푥) = 1 − ⌈ 1 ⌉ (17) 푛 ̅̅̅ ̅ 2 ∑푖=0(|푀−푂|+|푂−푂|) where M and O are simulated and observed variables (e.g., ADAP MDAP or HWAM), respectively.

3.3. Results and Discussion

3.3.1 Calibration

Initially, we calibrated CERES-Maize using the irrigated locations (Branch, Cass and

Mason) only in 2017. Well-watered and fertilized conditions are suggested for calibration purposes (Grassini et al., 2015). According to GDD requirement, the maize hybrid (PC0001-

2500-2600 GDD) (Cultivar code for CERES-Maize; Hoogenboom et al., 2013) selected for starting the search of parameters (GENCALC only) is suitable for the study area (Prokopy et al.,

2017). Table 3.6 shows the calibrated genetic coefficients using only one cropping season (2017) in the calibration. Based on Tables 3.4 and 3.6, GENCALC only changed the values of P1 and

P2, respectively. Other coefficients from PC0001-2500-2600 GDD did not change. In GLUE, the calibrated value of P1 was reduced compared to the selected maize hybrid, which suggests that the juvenile stage should reach three to four days earlier and hence anthesis.

However, NMCGA (SD and NO_SD) estimated an increased P1 value, which suggests that the juvenile stage needs more degree-days to complete hence anthesis is delayed. However, a lower P2 value was estimated, which signifies that any delay in anthesis is compensated as there is little delay in the developmental process if photoperiod is increased above the maximum physiological limit of 12.5 hrs (Jones and Kiniry, 1986; Hanks and Ritchie, 1991 and Román-

Paoli et al., 2000). For all methods, it was observed that there are low estimates of P5, which signify that the calibrated cultivar requires lesser thermal time from anthesis to physiological maturity compared to PC0001 (2500-2600 GDD). G2 values are relatively low especially for

NMCGA_NO_SD. Modern hybrids should have a maximum kernel number as close to 800 (Du

Toit, 2002). For a more objective comparison, we evaluated CERES-Maize performance using the calibrated coefficients.

Table 3. 6. Genetic coefficients estimated by GENCALC, GLUE, NMCGA and Ensembling approach in 2017. Methods P1 P2 P5 G2 G3 GENCALC 168.3 0.539 780.0 750.0 8.50 GLUE 147.2 1.011 638.4 701.73 12.28 (33.796) (0.638) (33.142) (188.74) (2.749) NMCGA_SD 199.1 0.347 574.2 774.9 11.49 (9.651) (0.238) (84.179) (23.090) (0.251) NMCGA_NO_SD 199.4 0.289 666.5 579.7 16.50 Arithmetic Average 178.5 0.546 664.8 701.6 12.19 Weighted Average 185.2 0.560 650.9 719.3 12.07 Note: values in parentheses are standard deviations of parameter estimates

Table 3. 7. Performance of CERES-Maize using estimated parameters by GENCALC, GLUE, NMCGA and ensembling approach during calibration in 2017. ADAP (Day) MDAP (Day) HWAM (Kg.ha-1) MBE RMSE d- MBE RMSE d- MBE RMSE d- index index index GENCALC -0.33 2.89 0.78 6.67 11.27 0.37 360.33 552.17 0.72 GLUE -1.33 3.87 0.89 5.83 11.38 0.39 961.00 1514.28 0.32 NMCGA_SD -1.17 4.06 0.86 4.00 8.74 0.40 29.00 361.31 0.70 NMCGA_NO_SD -1.17 4.06 0.86 -1.17 4.10 0.72 492.50 935.61 0.48 Arithmetic -1.00 3.51 0.80 3.00 6.24 0.47 803.17 1246.14 0.40 Average Weighted -1.00 3.51 0.80 3.00 6.24 0.47 424.50 635.70 0.68 Average

Table 3.7 shows that all methods performed well in simulating ADAP). However, most of the methods did not perform well in simulating MDAP and yield. This modest performance could be attributed to the length of the data used in the calibration. Development of genetic coefficients of a cultivar for a specific agro-climatic environment requires adequate data from multiple locations and cropping seasons to include environmental variability to the genotype, environment and management interactions (Kersebaum et al., 2015; He et al., 2017). In order to obtain representative parameter estimates, it is better to use multiple years of calibration data

(Bulatewicz et al., 2009; Confalonieri et al., 2016; Seidel et al., 2018). In order to improve the calibration, parameter uncertainty has to be minimized, which can be done by reducing uncertainty in input like soil properties, initialization variables and management practices

(Wallach et al., 2012; Dzotsi et al., 2015; Varella et al., 2012; Roux et al., 2014; Waha et al.,

2015). We then re-run the calibrations using 2017 and 2018 data from the irrigated locations.

The genetic coefficients calibrated from the two-year datasets are given in Table 3.8. CERES-

Maize performance is shown in Table 3.9.

Table 3. 8. Genetic coefficients estimated by GENCALC, GLUE, NMCGA_SD, NMCGA_NO_SD and Ensembling approach in 2017 and 2018. Methods P1 P2 P5 G2 G3 GENCALC 143.8 0.539 780.0 750.0 8.50 GLUE 133.2 1.690 767.5 762.2 12.58 (30.673) (0.453) (40.102) (176.24) (2.663) NMCGA_SD 134.4 1.714 758.6 779.7 13.15 (8.356) (0.202) (73.134) (20.574) (0.191) NMCGA_NO_SD 134.9 1.714 666.4 806.6 14.83 Arithmetic Average 136.6 1.414 743.1 774.6 12.27 Weighted Average 137.5 1.414 749.0 777.4 11.37 Note: values in ( ) are standard deviation of parameter estimates

3.3.1.1. Anthesis and physiological maturity: calibration results

Overall, CERES-Maize performance in simulating phenology have improved after calibration using two years of data (see Tables 3.7, 3.9). Due to lower P1 values in the new calibration (see Tables 3.6, 3.8), anthesis dates (ADAP) were slightly under-predicted in all the methods, which is reflected by the negative MBE (Table 3.7, 3.9). GENCALC, GLUE,

NMCGA_SD and NMCGA_NO_SD under-predicted anthesis. Except for NMCGA_NO_SD, all the methods over-predicted maturity (Table 3.9). After re-calibration however, d-index for

ADAP and MDAP have improved substantially for all the methods. Poor optimization of parameters P1, P2 and P5 possibly have caused the deviations in predicted phenology. Especially for P5, which determines the growth of the cultivar after anthesis and hence maturity, might not be optimized (see Hanks and Ritchie, 1991; Román-Paoli et al., 2000). P5 represents thermal time from anthesis to physiological maturity and it varies from 700-1000 GDD for modern cultivars (Román-Paoli et al., 2000).

Phenological impacts however are the results of the combined effects of all phenological parameters, hence improper calibration of the phenological parameters will create a slight difference in GDD that can affect anthesis and maturity (Román-Paoli et al., 2000). Coefficient

33 of determinations (R2) between predicted and observed anthesis dates were found moderate for all the methods (Fig. 3.5). Overall, however, all the methods under-predicted anthesis in 2017 and 2018 at Branch and Cass, but over-predicted at Mason for both years (Table 3.9). This result reflects the inter-annual variability in predicting anthesis. Variability in genetic expressions depends greatly on the variations in weather parameters, especially solar radiation during the growing season (Lee et al., 2016). Mason has higher amount of accumulated solar radiation than

Branch and Cass (Table 3.2). Coefficient of determinations (R2) for maturity dates were found higher for all methods (Fig. 3.5).

Table 3. 9. Performance of CERES-Maize using parameters estimated by GENCALC, GLUE, NMCGA and ensembling approach during calibration in 2017 and 2018. ADAP (DOY) MDAP (DOY) HWAM (Kg.ha-1) MBE RMSE d- MBE RMSE d- MBE RMSE d- index index index GENCALC -0.83 3.98 0.93 5.50 12.19 0.84 603.67 783.64 0.96 GLUE -1.83 4.74 0.89 4.00 12.42 0.84 1065.1 1093.2 0.93 NMCGA_SD -1.67 4.90 0.88 2.17 10.06 0.87 80.67 665.09 0.97 NMCGA_NO_SD -1.67 4.90 0.88 -7.83 10.86 0.84 594.00 833.42 0.95 Arithmetic Avg. -2.17 4.45 0.91 -0.50 8.75 0.90 860.83 1106.8 0.90 Weighted Avg. -2.17 4.45 0.91 -0.50 8.75 0.90 801.00 936.96 0.94

3.3.1.2. Yield calibration results

The growth parameters G2 and G3 directly control yield whereas P5 and P2 control it indirectly. The reproductive growth stages and yield components (e.g., dry matter, grain size and canopy weight) are controlled by P5, G2, G3 and PHINT and their interactions (Hammad et al.,

2018). However, G2 is the most critical parameter in predicting yield (Ritchie and Wei, 2000; Du

Toit, 2002; Ritchie and Alagarswamy, 2003; Lizaso et al., 2007), which is dependent on the sowing date and associated weather variability (Zhou et al., 2017). G2 values increased during re-calibration and its impact was manifested in yield improvements in all the methods (Table 3.8,

3.9). GENCALC predicted yield with d-index of 0.96 and RMSE of 783.64 kg ha-1 while GLUE predicted yield with d-index of 0.93 and RMSE of 1093.2 kg ha-1. NMCGA_SD outperformed all the methods, with predicted yield d-index of 0.97 and RMSE of 665.09 kg ha-1. (Table 3.9;

Fig 3.5). Coefficient of determinations (R2) of yield were high showing a strong confidence of the model in the simulation of yields (Fig. 3.5). Yearly variations of ADAP, MDAP and yield were attributed to the variability in weather and other management practices (e.g., Confalonieri et al., 2016; Waha et al., 2015).

3.3.1.3. Calibration results from ensemble of methods

As shown above, the individual methods performed differently in predicting phenology and yield. Some are better in predicting phenology and some are better in predicting yield.

However, we wanted to calibrate genetic coefficients that are robust and resilient. As Vrugt and

Robinson (2007) noted the advantage of ensembling models, we performed ensembling of the coefficients, ran them in CERES-Maize, and compared their performance with the individual methods.

We employed weighted and arithmetic averaging to ensemble the coefficients (Section

2.3.5). In the weighted averaging method, weights were assigned to the parameters based on the distance between the predicted and observed variables that they mostly influenced e.g., ADAP,

MDAP and HWAM. In the initial calibration (i.e., using only 2017 data), ensembled coefficients performed poorly, especially for MDAP and yield, as individual methods. With the re-calibration using 2017 and 2018 data, their performance improved significantly, outperforming some of the methods (Table 3.7, 3.9). Overall, NMCGA_SD performed best in predicting yield (d-index =

0.97) (Table 3.8). However, weighted averaging performed relatively better in predicting phenology (anthesis and maturity) and comparable in predicting yield (Table 3.8). Arithmetic

35 averaging of the coefficients also performed well. Our previous study calibrating rice varieties in the Philippines suggested a better performance of arithmetic averaging (Jha et al., 2019, in review). Nevertheless, these results corroborate the value of using multiple methods in crop model calibration and ensembling the derived parameters for better performance.

Figure 3. 5. CERES-Maize performance after calibration of GENCALC, GLUE, NMCGA_SD, NMCGA_NO_SD, arithmetic and weighted averaging for 2017 and 2018.

3.3.2 Potential yields from calibrated genetic coefficients

The potential yields indicate that the crop model can simulate the genotype expression without stress. The potential yield of a crop cultivar is determined by climatic parameters

(temperature, solar radiation) and genetic characteristics only (Andrea et al., 2018). Potential yields can be achieved in a given climate under non-stressed conditions (water, nutrients, biotic).

However, this is hard to achieve in real world conditions that is why agronomists and breeders always aim to optimize agronomic management to reduce the gap between actual (observed) and potential yields (Lobell et al., 2009). Fig. 3.6 shows that the genetic potential can attain the levels of observed yields in the rainfed locations (Washtenaw, Allegan, Ingham, Saginaw, Huron and

Montcalm). Note that the rainfed (validation) locations were not included in the calibration.

Figure 3. 6. Potential yields and observed yield for 2017 and 2018 for all maize trial locations using weighted averaging of genetic coefficients.

3.3.3 Validation

The calibrated crop coefficients were validated at the six-rainfed locations (Washtenaw,

Allegan, Ingham, Saginaw, Huron and Montcalm; see Table 3.1) for two years (2017 and 2018).

The representative soil characteristics used in calibration were also used in validation. The validation results for phenology (anthesis and maturity) showed that the model could perform well, with d-index varying from 0.84 to 0.95 for all methods (Table 3.10). However, yields were substantially under-predicted, with high MBE (negative) and RMSE, and low d-index (Table

3.10).

Table 3. 10. Performance of CERES-Maize using parameters estimated by GENCALC, GLUE, NMCGA and ensembling approach during validation at rainfed locations with soil profiles used in calibration. ADAP (DOY) MDAP (DOY) HWAM (Kg.ha-1) MBE RMSE d- MBE RMSE d- MBE RMSE d- index index index GENCALC 0.33 2.89 0.84 6.33 7.96 0.76 - 4924.03 0.19 4446.3 GLUE -1.00 3.21 0.87 3.33 5.13 0.91 - 3877.22 0.26 3315.5 NMCGA_SD -0.50 2.42 0.92 3.50 4.60 0.93 - 4843.11 0.21 4400.0 NMCGA_NO_SD -0.50 2.42 0.92 -5.83 6.96 0.81 - 4053.51 0.26 3593.0 Arithmetic Avg. -1.17 3.08 0.85 1.00 3.37 0.95 - 4186.99 0.25 3576.3 Weighted Avg. -1.00 2.89 0.87 1.50 3.76 0.94 - 4494.99 0.23 3838.8

3.3.3.1. Soil Root Growth Factor (SRGF) adjustment

In Section 3.1.4, we showed that the potential yields simulated from the rainfed locations

(Fig 3.6; see Table 3.1) using the calibrated genetic coefficients are capable of attaining the levels of observed yields. However, the model structure used in irrigated conditions and imposed on water stressed environments could restrict the crop from exploring available resources from 39 the soil. Note that the soil structure model in DSSAT is static. Varella et al (2012) explained that uncertainties in soil input parameters can influence model performances. Water and nutrient availability in the root zone have a substantial impact on root geometry, dynamics and physiology, therefore influencing plant water and nutrient uptakes and yields (Ritchie et al.,

1998; Ma et al., 2006).

Maize root grows deeper in the soil to extract more water per unit length of root in rainfed than irrigated conditions (Sharp and Davies, 1985). These root dynamics are more prevalent in the event of stress during critical periods e.g., tasselling to grain filling in maize

(Lorens et al., 1987; Wan et al., 2000; Vamerali et al., 2003, Hund et al., 2009; Garcia et al.,

2009). Lenka et al. (2009), Panda et al. (2004) and Djaman and Irmak (2012) noted that in well- irrigated conditions, root water extraction mostly takes place from the top soil layers.

Based on our literature review and multiple model iterations we suggest that the root growth factors in the soil model as used in irrigated conditions may be restricting the roots to go deeper/wider when applied under water stressed conditions (e.g., López-Cedrón et al., 2008;

Yang et al., 2009).

The soil database in DSSAT soil module has a root growth factor (SRGF) parameter, which controls the maximum rooting depth and root mass distribution in the soil profile (Jones and Kiniry, 1986). The root growth distribution function in CERES-Maize has been calibrated for different soil types as root hospitality factor, which gives flexibility to root growth in the model according to soil water availability and structure (Friasse et al., 2001). In DSSAT v4.6,

SRGF with a value of 1.0 allows the root to grow equally in the soil layer and gradually decreases to zero in tapered form through the deeper layer (Yang et al., 2017). Table 3.11 and

3.12 show the SRGF vertical distributions of the sandy loam and loam soils used in calibration

(irrigated locations) and validation (rainfed locations).

Table 3. 11. Soil root growth distribution factor (SRGF) of the sandy loam soil used in calibration and validation. Montcalm Sandy Loam Soil Layer Depth (cm) SRGF (Calibration) SRGF (Validation) Irrigated Rainfed 0-10 1.0 1.0 10-25 1.0 1.0 25-40 0.5 1.0 40-65 0.1 0.8 65-90 0.1 0.6 90-115 0.1 0.4 115-140 0.1 0.2 140-165 0.1 0.0 165-190 0.1 0.0 190-215 0.1 0.0

Table 3. 12. Soil root growth distribution factor (SRGF) of the loam soil used in calibration and validation. Kalamazoo Loam Soil Layer Depth (cm) SRGF (Calibration) SRGF (Validation) Irrigated Rainfed 0-10 1.0 1.0 10-22 1.0 1.0 22-31 1.0 1.0 31-41 0.9 1.0 41-51 0.7 1.0 51-61 0.5 1.0 61-75 0.3 1.0 75-89 0.1 1.0 89-102 0.1 0.8 102-120 0.1 0.6 120-140 0.1 0.4 140-160 0.1 0.2 160-180 0.1 0.0 180-200 0.1 0.0

Based on a loam soil profile developed for Iowa, U.S.A. (see DSSATv4.6 SOIL.SOL by

Ritchie for Iowa; Hoogenboom et al., 2003), roots were allowed to fully grow by keeping

SRGF=1.0 until the soil depth of 90 cm, then slowly tapering down until the depth of 190 cm, where SRGF=0. For our sandy loam soil, we revised the SRGF parameters by relaxing SRGF to

1.0 until 40 cm depth then slowly tapering down to 0.2 until 140 cm (Table 3.11). For the loam soil, SRGF values were relaxed to 1.0 until 89 cm then slowly tapering down to 0.2 until 160 cm, except for Ingham (Table 3.1, 3.12) where we allowed the root to grow deep until 200 cm.

These revised soil profiles were used in validation and CERES-Maize predictions of yields were substantially improved (Table 3.13). We analysed the new validation results to evaluate our hypothesis about the adaptive capacity of the plant under stressed environments that static model parameters may restrict. Fig. 3.7a suggests yield improvement from 4,058 kg ha-

1with the old soil profile to 11,137 kg ha-1with the revised one, which is close to the 10,984 kg/ha observed yield. The improvement in yield prediction was an indication that some structural components in the model set up need to be adjusted when applied under rainfed conditions. The plant’s feedback (or response) mechanisms to water-stressed environment, e.g. root expansion and lengthening, might not be explicitly accounted for in the model, particularly on the flexibility of the soil capacity to allow roots to wander deeper and wider in the root zone. Currently, this is strictly restricted by the soil profile set up a-priory to simulations. The observed yields are our

“gold standard data” and matching them together with observed phenology supports our attempt to adjust a static soil structural property in order to get a better fit of the data.

Figure 3. 7. Model performance ((root depth, water and nitrogen stress andyield (a)) and (root length density (b,c))) in 2017 at Washtenaw (rainfed) under old (b) and revised soil (c) profiles.

Physiologically, maximum root growth occurs during pre-anthesis period to provide enough water for crop growth and development (Liu et al., 2017; Yang et al., 2017). In the case of the revised soil profile, root growth ceased after anthesis (78 DAP), which is typically 5-7 days after tasselling (Fig. 3.7a, 3.7c), which helps in the reduction of water stress during tassel initiation to early grain filling period (e.g., Lizaso et al., 2018). The root length density of each soil layer is uniform in the case of the revised soil profile (Fig. 3.7b, 3.7c). The root layer density suggests that the deeper root layers (8 and 9) are able to extract water and nutrients during the peak demand period of crop growth (Fig 3.7c). This is restricted in the old soil profile configuration (Fig 3.7b) as roots in soil layers 7 and 8 grew only after tasselling and anthesis and

43 did not grow deep enough, hence were not able to support the peak demands for water and nutrient in the growing season (Fig. 3.7a, 3.7b).

We also performed simulations under irrigation with the revised soil profile to evaluate if relaxing SRGF contributed to yield improvement when water is readily available. We found that there was no substantial difference in predicted yields under irrigated conditions before and after relaxing SRGF values (Fig 3.8a). Root length density in the deeper layers however became more uniform after tasselling after relaxing SRGF values (Fig 3.8c), while the deeper roots in layers 9 and 10 continued growing after anthesis with the older soil profile, but that did not contribute much to the yield dynamics as sufficient water was already provided by irrigation (Fig. 3.8a, 3.8b). The revised soil profiles were used in the validation of the derived genetic coefficients for all rainfed locations in 2017 and 2018. Sections 3.2.2 and 3.2.3 show the performance of phenology and yield predictions with the revised soil profile configurations.

Figure 3. 8. Model performance ((Root depth, water and nitrogen stress, yield (a)) and (root length density (b,c))) in 2017 at Cass (irrigated) under old (b) and revised soil (c) profiles.

Table 3. 13. Performance of CERES-Maize using parameters estimated by GENCALC, GLUE, NMCGA and ensembling approach during validation at rainfed locations with the revised soil profiles. ADAP (DOY) MDAP (DOY) HWAM (Kg.ha-1) MBE RMSE d- MBE RMSE d- MBE RMSE d- index index index GENCALC 0.17 2.74 0.96 6.75 8.49 0.87 332.25 653.96 0.93 GLUE -1.50 3.03 0.96 3.17 4.97 0.96 645.58 774.23 0.92 NMCGA_SD -0.50 2.65 0.97 3.67 5.16 0.95 323.67 536.84 0.96 NMCGA_NO_SD -0.50 2.65 0.97 -4.42 5.63 0.94 391.58 636.98 0.94 Arithmetic Avg. -1.25 2.84 0.96 0.58 2.99 0.98 561.83 732.40 0.92 Weighted Avg. -1.25 2.84 0.96 1.08 3.12 0.98 419.92 577.99 0.96

3.3.3.2 Anthesis and maturity validation results

Predictions of anthesis (ADAP) and maturity dates (MDAP) did not change substantially when using the old (Table 3.10) or new soil profiles (Table 3.13) in the validation. For all the methods, anthesis dates were slightly underpredicted but with high d-index (Table 3.13). It suggests that the calibrated P1 and P2 coefficients performed well in predicting anthesis at the rainfed locations. Overpredictions of maturity dates by 3 days (GLUE and NMCG_SD) to a week (GENCALC), and underprediction by 4 days (NMCGA_NO_SD) were also observed

(Table 3.13). Ensembling methods performed better than individual methods in predicting maturity dates (Table 3.13). Coefficient of determinations (R2) in Fig. 3.9 also show that the model could predict anthesis with high accuracy. Overlapping phenological data in some locations can be observed (e.g., Saginaw 2018 and Huron 2017) and is due mainly to the interannual variability of crop response to planting dates and weather patterns. Ensembling methods have the highest R2 values for MDAP prediction (Fig. 3.9).

3.3.3.3 Yield validation results

Tables 3.10 and 3.13 suggest that majority of the impacts from enhancing root dynamics is accounted for by the improvements in yields and not on phenology (see Section 3.2.2). The yield d-index for all methods improved from <0.3 (Table 10) to >0.9 (Table 13) and R2 > 0.8

(Fig. 3.9). Yields were still over predicted with RMSE ranging from 536 to 774 kg/ha (Table

3.13). Allowing the root system to explore more resources allowed CERES-Maize to match better the observed yields under rainfed conditions, which is our gold standard data for fitting the model. This result suggests the intrinsic limitation of a rigid soil-based root growth factor

(SRGF) (i.e., not dynamic) if one assumes it blindly and does not account for the adaptive capacity of the crops when setting up a soil profile data under water stress conditions.

Figure 3. 9. CERES-Maize performance for validation of calibrated coefficients of GENCALC, GLUE, NMCGA_SD, NMCGA_NO_SD, arithmetic and weighted averaging for 2017 and 2018.

3.4 Summary and Conclusions

In this study, we employed existing (GENCALC, GLUE) and new methods

(NMCGA_SD and NMCGA_NO_SD) of crop model calibrations, ensembled them to get a robust estimates of a hybrid maize genetic coefficients. The genetic coefficients of CERES-

Maize were calibrated using three irrigated locations (Branch, Cass and Mason) in Michigan during 2017 and 2018 growing seasons. Results suggest that using two years (2017-2018) of data for calibration gave better results than using only one-year data (2017). The phenological parameters were mostly affected by the inclusion of 2018 data in the calibration. The calibrated coefficients were used to evaluate CERES-Maize performance across the six-rainfed locations

(Washtenaw, Allegan, Ingham, Saginaw, Huron and Montcalm) during 2017 and 2018 growing seasons. The estimated coefficients were able to produce potential yields that are higher that observed yields, suggesting that they can generate those levels of yields if applied in real-world conditions. However, under stressed environments, validation results suggested that there are mechanisms that are not accounted for or permitted by the irrigated model setup used in calibration on the adaptive capacity of a crop grown under rainfed conditions, particularly root dynamics. Based on literature reviews and this above hypothesis, we relaxed the capacity of the soil to allow root geometry to change from the irrigated setup, which resulted in a better model fit. Using similar soil model structure in 2018, the calibrated parameters performed well under rainfed conditions. The revised soil model structure was cross validated under irrigated conditions with nearly similar results as the old soil model structure suggesting that the root system did not change substantially between the two soil model models when the crops were irrigated, possibly due to the abundance of water in the upper root zone.

We found that ensembling coefficients by weighted averaging, based on the inverse squared distances between the predicted and observed variable(s) (e.g., ADAP, MDAP or

HWAM), outperformed most of the calibration models. The purpose of ensembling genetic coefficients from several methods was to integrate the strengths of the methods with a goal of achieving a set of parameters (i.e., genetic coefficients) that is robust and resilient. Our results suggest that there is value in using multiple methods in calibrating crop models and extracting the best information from them.

3.5 Acknowledgment

This work is partly funded by the Corn Marketing Program of Michigan (CMPM) and

MSU AgBioResearch. We acknowledge Chubu University, ListenField, USAID-Philippines and

NASA-SERVIR for partly funding the first author’s research work at MSU. We also acknowledge Bill Widdicombe for the Michigan Maize Performance Trial (MMPT) experiments and Katlin Fusilier for collecting specific field data.

4. ESTIMATION AND VALIDATION OF REMOTELY SENSED

EVAPOTRANSPIRATION FOR THE DEVELOPMENT OF CROP COEFFICIENTS

OF MAIZE AND IRRIGATION SCHEDULING

4.1 Introduction

Crop production consumes the largest proportion of fresh water through irrigation (Döll et al., 2014; Wada and Bierkens, 2014; Haddeland et al., 2014). Increasing use of irrigation and associated water withdrawals for agriculture have led to unsustainable water use and a number of environmental consequences (Rosa et al., 2018) and in the near future, irrigation water scarcity is expected to increase (IPCC, 2014; Lu and Kueppers, 2015; Porter et al., 2017).

Irrigation during critical crop growth stages helps in meeting the crop water demand and minimizing the yield volatility (Irmak et al., 2016; Williams et al., 2016; Jha et al., 2018; Comas et al., 2019). Simulation studies have shown that if irrigation were eliminated, the global cereal production would decrease by ~20 % (Siebert and Döll, 2010; Siebert et al., 2015;

AQUASTAT, 2018). Seasonal variation in precipitation especially in the peak-growing season in humid to sub-humid regions makes water management challenging (Siebert and Doll, 2010).

On the other hand, irrigation influences the local crop micrometeorology through energy fluxes in and over the crop canopy (Siebert et al., 2014; Thiery et al., 2017; Sridhar and

Anderson, 2017; Zhang et al., 2019) and the regional climate conditions through water and energy interactions (Han et al., 2014; Keune et al., 2018; D’Odorico et al., 2018). Globally, irrigation contributes 418-1233 km3 year-1 of water to the atmosphere through evapotranspiration (ET), the largest sink of irrigation water, most of which returns to the irrigated areas as recycled precipitation (Thiery et al., 2017). In the temperate climate of

Midwest US, two-thirds of precipitation returns to atmosphere through ET and the majority of

50 that occurs during the crop-growing season (Abraha et al., 2015). ET uniquely links water, carbon, and the energy cycle and is considered an important predictor of crop yield (Abtew and

Melesse, 2013; Li et al., 2014; Fisher et al., 2017; Yang et al., 2018) and in determining water use efficiency (WUE) (Ito and Inatomi, 2012; Knauer et al., 2018). To increase WUE, an improved irrigation management strategy with a reliable ET estimate is necessary.

Because of its importance in water management, ET has been studied extensively. ET can be measured by lysimeter, eddy covariance (EC), or the Bowen-ratio energy methods at the field scale (Bowen, 1926; Tomlinson, 1996). Due to scaling and interactions of landscape features with climate, ET at the regional scale is more difficult to quantify accurately using in- situ methods (Abraha et al., 2015; 2016). With advances in thermal satellite remote sensing

(RS), ET patterns have been observed over time and space (Hamada et al., 2015; Anderson et al., 2018). ET either can be estimated with RS through the surface energy balance method (EB) or with reflectance based crop coefficients (Kcr). The EB uses remotely sensed surface reflectance in the visible (VIS) and near infrared (NIR), and surface temperature in the infrared

(IR) regions of the electromagnetic spectrum (Holmes et al., 2018). In contrast, Kcr is derived through vegetation indices (Glenn et al., 2011). Widely used EB based ET models include the

Atmosphere-Land Exchange Inverse (ALEXI) model (Anderson et al., 1997); the Surface

Energy Balance Algorithm for Land (SEBAL) model (Bastiaanssen et al., 1998), the Surface

Energy Balance System (SEBS) model (Su, 2002), the Mapping EvapoTranspiration at high

Resolution with Internalized Calibration (METRIC) model (Allen et al., 2007), the Simplified

Surface Energy Balance operational (SSEBop) model (Senay et al., 2013) and the Backward‐

Averaged Iterative Two‐source Surface temperature and energy balance Solution (BAITSSS)

(Dhungel et al., 2019).

These ET estimation methods have their own uncertainties associated with data inputs, model structures and abstractions (Zhu et al., 2016). In order to minimize associated uncertainties from individual methods, the use of multi-model ensembles have gained popularity in the past (e.g., Vrugt and Robinson, 2007). Ensembling crop models helped in better predicting yield and on the sensitivity and uncertainty analyses of input parameters

(Bassu et al., 2014; Huang et al., 2017; Basso et al., 2018; Rosenzweig et al., 2018; Kimball et al., 2019; Müller et al., 2019). Hydrological models (e.g., Velázquez et al., 2011; Wagena et al.,

2019) and climate models (e.g., Rötter et al., 2011; Chen et al., 2019) have been ensembled to minimize the uncertainty associated with individual models. Different ET models have been ensembled to assess their predictability over a wide range of climatic conditions and landscapes and have been used for irrigation scheduling (Garcia et al., 2013; Ershadi et al., 2014; Zhu et al.,

2014, 2016).

Scheduling of irrigation is done by estimating actual crop ET (ETcrop) and available water in the root zone. Traditionally, ETcrop is estimated by multiplying a crop coefficient (kc ) with a reference ET (Jensen et al., 1990; Allen et al., 1998). Crop coefficients are typically calculated based on crop development stages under optimum management conditions using lysimeter studies. These specific measurements do not describe the spatial variation in crop coefficients at the regional scale due to varying management practices (e.g., planting density, irrigation, tillage and others) and crop conditions (Marin et al., 2016). Characterization of kc at the larger scale using remote sensing can be helpful for better water management. Vegetation indices-based kc can be estimated through remote sensing and incorporated in irrigation scheduling (Hunsaker et al., 2003; Mokhtari et al., 2019). Kc can also be estimated inversely by estimating ET, since Kc is the ratio of actual ET to the reference ET (Allen et al., 2005). Crop

52 coefficients have been derived inversely by estimating ET through the SEBAL energy balance model (Michael and Bastiaanssen, 2000; Tasumi et al., 2005; Samani et al., 2008), in-situ ET measurements through the Bowen ratio method (Sobenko et al., 2019), lysimeter measurements

(Martínez-Cob, 2008 Piccinni et al., 2009) and eddy covariance measurements (Facchi et al.,

2013; Migliaccio et al., 2014; Corbari et al., 2017). However, all of these approaches of estimating crop coefficients were used to derive Kc for a single location or from a single ET model.

The main goal of this study is to derive reliable estimates of maize crop coefficients (Kc) in the irrigated region of Southwest Michigan by using an ensemble of ET models. Specific objectives are as follows: (i) to estimate actual ET of maize fields using RS based methods

(SEBAL, PT-JPL and MOD16), FAO-Kc and an ensembling approach, (ii) to derive crop coefficients (Kc) of maize from the remotely sensed ET, (iii) to validate the region specific dervived Kc using a soil water balance model, and (iv) to evaluate the region specific derived

Kc to design irrigation scheduling.

4.2 Materials and Methods

4.2.1 Study Area

The study sites are located in Southwest Michigan, which is the heartland of hybrid seed production for maize in the northeastern part of the US Midwest Maize Belt (MSU extension,

2014). The two maize fields selected for this study are equipped with eddy covariance towers

(sites in the AmeriFlux network; Cammalleri et al., 2014; Abraha et al., 2019) and are under a continuous rainfed maize crop rotation (Fig. 4.1). The first field (US-KL1, KBS Lux Arbor

Reserve Corn) was managed as maize-soybean crop rotation for more than 50 years, before conversion in 2009 to a no-till continuous maize rotation. It is located at the Long-term

Ecological Research site of W. K. Kellogg Biological Station, Kalamazoo, Michigan (42.49° N,

85.44° W, 288 m asl).

The second field (US-KM1, KBS Marshall Farms Corn; 42.44° N, 85.33° W, 286 m asl) was under the Conservation Reserve Program (CRP) Grasslands of smooth brome grass

(Bromus inermis L.) for 22 years before conversion in 2009 to a no-till continuous maize rotation. These two sites with different land-use history offer a special opportunity for ET analysis (Senay et al., 2019). Both fields are located in a humid continental temperate climate

(Koppen, 1900) six miles apart, with a mean total annual precipitation of 1027 mm, out of which more than half (523 mm) occurs during maize growing season (May-September) (Abraha et al., 2019). The dominant soil types are a well drained Kalamazoo (loam) and an Osthemo

(sandy loam) (Robertson and Hamilton, 2015; Luehmann et al., 2016).

Figure 4. 1. Eddy covariance towers at the maize fields

4.2.2 Data Collection

4.2.2.1 Weather Data

The weather data for this study were obtained primarily from the Enviro-weather network, a sustainable weather-based information system that helps growers and stakeholders in making farm related decisions in Michigan (Andresen et al., 2012). The reference evapotranspiration (ETref ) for estimating ETcrop were obtained from the nearest weather station,

Pierce Cedar Creek Institute, Hasting (42.53 N, 85.30 W, 283 m asl). Means and standard deviations of daily ETref (2010-2017) for the region are shown in Fig 4.2 showing mean ETref peaks in July.

Rainfall data were not used directly in ET estimation. However, being an important driver of ET dynamics at the spatiotemporal scale of the study, they were used to assess the reliability of the estimated ET. Rainfall data from the Leslie station site (42.47 N, 84.46 W; 291 m asl) nearest to our validation site Manchester, Michigan (42.19 N, 84.04 W) were used for simulating the water balance. For sensitivity analysis of the water balance model, rainfall input from the Hastings Enviroweather station site, a rain gauge at the field site and reanalysed rainfall estimates from NASA-POWER, version 2.1 Global Precipitation Climate Project

(GPCP – 1DD), were also collected (Bolvin et al., 2009).

Figure 4. 2. Mean reference ET (2010-2017) from the nearest weather station (Hasting, Michigan) in the study area.

4.2.2.2 Soil moisture and irrigation data

Validation of the derived crop coefficients were done using observed soil moisture in the maize field at Manchester (42.19 N, 84.04 W). The soil texture at this field was sandy clay loam. Rainfall was measured by an ATMOS 41 weather station (Meter Group, Pullman, WA)

(Fig 4.3a) along with air temperature, relative humidity, vapour pressure, barometric pressure, wind speed and direction, solar radiation, and lightning strike frequency. The rain gauge was adjusted at two week intervals to ensure that it remained over the canopy all the times during the growing season. The measured rainfall from the rain gauge installed at the field was used for the sensitivity analysis of the water balance model. Actual irrigation amounts applied were obtained from grower observations at the field site, from July 1 to August 16, 2019, which coincided with tasseling to milking stage in maize (Abendroth et al., 2010). Volumetric soil moisture, soil temperature, and electrical conductivity were monitored with five Teros 12

56 probes (Meter Group, Pullman, WA) at 15, 30, 45, 60, and 90 cm depths. Sensors were installed by digging a shallow trench and inserting the sensors horizontally into the soil, then backfilling the trench (Fig 4.3b). The resolution and accuracy of the volumetric moisture sensor are 0.001 m3m-3 and  0.02 m3m-3, respectively. The measurements of the soil moisture sensors and the weather parameters were recorded hourly using an EM 60G datalogger (Meter Group, Pullman,

WA; Fig 4.3a), and converted into daily variables.

(a) (b)

Figure 4. 3. Rain gauge (a) and soil moisture sensors (b) at the maize field in Manchester, Michigan.

4.2.2.3 Eddy covariance (EC) data

EC sensors were placed at 2.0 m above the average canopy height at the centre of the each of the two maize fields (Abraha et al., 2019). The locations of the two towers are shown in 57

Fig 4.1. ET data were computed as 30 minutes averages using EdiRe software (University of

Edinburgh, v 1.5.0.32, 2012) (Clement, 1999). The processed ET data were passed through quality checks and controls, and gap-filled using REddyProc (Wutzleret al., 2018), which follows the algorithm of Reichstein et al., (2005). The estimated daily ET data from the flux towers were used for validation and as a reference for ensembling ET methods using an Inverse

Distance Weighting (IDW) method, which will be described later.

4.2.2.4 Remote sensing data

Due to the complexity of hydrological process and heterogeneity of natural environments, it is difficult to estimate ET over large areas with in situ measurements. Remote sensing-based ET estimates for this study were generated using SEBAL (Bastiaanssen et al.,

1998), PT-JPL (Fisher et al., 2008) and PM-MODIS (ET) (Mu et al., 2011).

4.2.2.5 SEBAL

Based on the coordinates of the two maize fields, the tile grid (sinusoidal projection) of

MODIS (Moderate Resolution Imaging Spectroradiometer) data products were obtained using the MODLAND tile calculator. The spatial domain of the study area lies within horizontal 11 and vertical 04 sinusoidal tiles. MOD09GA version 6 (Daily surface reflectance products L2G;

Band 1 to 7 at 500 m resolution) and MOD11A1 version 6 (Land surface temperature & emissivity product at 1 km resolution) for the years (2010-2017) were downloaded from

NASA’s LPDAAC (Land Processes Distributed Active Archive Center)

(https://lpdaac.usgs.gov/). The downloaded data in hierarchical data format (.hdf) were geo- registered and downscaled at 500 m resolution using MODIS Reprojection Tool (MRT) under

MODIS conversion toolkit (MCTK) in ENVI Classic 5.3 software. Daily wind speed data were collected from the nearest weather station, Pierce Cedar Creek Institute, Hasting. We used these

58 data to estimate ET using our C-program implementation of the Surface Energy Algorithm for

Land (SEBAL).

4.2.2.6 PT-JPL

The modified Priestley-Taylor ET is a Level-3 (L-3) product of the ECOsystem

Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission, which is a combination of the land surface temperature product and ancillary data products (Hulley,

2015; Fisher et al., 2011; 2017). The NASA-JPL group provided daily ET estimates (2010-

2017) in GeoTiff format for validation and application in this study. It estimated actual ET with inclusion of plant- and soil-based eco-physiological stress factors. Surface latent heat flux is partitioned into soil evaporation, canopy transpiration and evaporation from intercepted moisture. These products are validated using FLUXNET and are ready-to-use product, hence, no calibration or ground data was required (Fisher et al., 2008).

4.2.2.7 PM-MODIS (MOD16A2- ET)

The MOD16A2 ET version 6 (500 m resolution) datasets were downloaded from the data repository of the USGS-EarthExplorer website. These downloaded data in hierarchical data format (.hdf) were geo-registered using the MODIS Reprojection Tool (MRT) under MODIS conversion toolkit (MCTK) in ENVI Classic 5.3 software and then converted into ENVI standard data (.dat) with header (.hdr) files. Annual time series of ET estimates were extracted for further analysis. The MOD16 ET estimates are based on Penman-Monteith equation

(Monteith, 1965, Mu et al., 2011). ET are available as 8-day composites (Mu et al., 2011).

4.2.3 ET models descriptions

4.2.3.1 SEBAL

ET is driven by the flux of energy at the surface. The Surface Energy Balance Algorithm for Land (SEBAL) developed by Bastiaanssen et al (1998, 2000, 2005) estimates instantaneous

ET for each pixel of the processed image as a residual from the energy balance equation (Eq. 1) considering latent heat as a proxy of ET:

퐿퐸 = 푅푛 − 퐺 − 퐻 (1) where, LE is the latent heat flux (W m-2), Rn is the net radiation flux (W m-2), G is the soil heat flux (W m-2), and H is the sensible heat flux to the air (W m-2). All the components in Eq. 1 are computed by the SEBAL model using inputs of surface temperature (Ts), albedo (α) and emissivity (ε) along with normalized differential vegetation index (NDVI), and leaf area index

(LAI).

Net radiation is the difference of incoming and outgoing fluxes computed using the surface radiation balance equation described by Zhou et al. (2017). Sensible heat (H) is computed through air and vapor momentum resistances (surface roughness) and the vertical temperature gradient (dT), which are functions of the wind speed and air temperature, respectively, known as the aerodynamic function (Allen et al., 2007). The air temperature is estimated as a proxy of surface temperature at the cold pixel (Bastiaanssen et al, 2005;

Senkondo et al., 2019) and wind speed (at 2 m) obtained from the nearest station were used to compute heat and vapor transport for sensible heat (H) computation. In order to estimate dT, the model has to select anchor (hot and cold) pixels, either manually or automatically. Manual selection is subjected to user-based uncertainty of choices and selection; therefore, we employed

SEBAL for automatic selection of the pixel. 60

The latent heat flux is divided by the latent heat of vaporization (~2.45 MJ Kg-1) to estimate instantaneous ET. The instantaneous ET is scaled up to 24 hours to get daily ET estimates assuming constant evaporative fraction throughout the day. This fraction expresses the ratio of actual to potential evaporative demand and hence is used in estimating daily ET (Farah et al., 2004). The SEBAL technique has been used and applied successfully in a number of previous studies related to ET estimation and water management (Bastiaanssen et al., 2005;

Allen et al., 2011; Singh and Senay, 2015; Grosso et al., 2018; Gobbo et al., 2019). Tang et al.

(2013), for example, found daily estimates of ET derived from SEBAL have an accuracy of

85%, with 95% for seasonal estimates, and 96% for annual estimates as compared to measured

ET from eddy covariance methods.

4.2.3.2 PT-JPL

A form of the Modified Priestley - Taylor method ( Priestley and Taylor, 1972 ), popularly known as PT-JPL and developed by the NASA Jet Propulsion Laboratory estimates actual ET through the introduction of plant- and soil-based eco-physiological stress factors

(Fisher et al, 2008; 2017). The idea of introducing these stress factors is to scale down the potential ET to actual ET in data scarce regions. It follows the two sources of evaporation (Soil and Crop) and combines plant moisture and temperature constraints, which helps in estimating daily variability in ET due to plant stress. It is a ready-made product generated by combining datasets from the International Satellite Land-Surface Climatology Project (ISLSCP; Los et al.,

2000) and the Advanced Very High Resolution Spectroradiometer (AVHRR; Houborg and

Soegaard, 2004), along with the original PT method with Priestley–Taylor coefficient (α) constant at 1.26 (Fisher et al., 2008). The model has been validated through FLUXNET measured ET data at different ecological environments (Baldocchi, 2019) and does not require

61 site calibration. The daily ET estimates from PT-JPL is in TIFF format and hence there is no need to reproject or georeferenced the data before processing them in ENVI. It has an advantage over SEBAL for having fewer cloud days as cloud corrections were performed using parameters from Cloud Climatology Project (CCP) data (Rossow et al., 1996)

4.2.3.3 MOD16ET

The MOD16, global 8-day ET (MOD16A2) datasets at 500 m resolution are based on the Penman-Monteith relationship for ET (Monteith, 1965), which uses reanalyzed daily meteorological data from NASA’s Global Modeling and Assimilation Office (GMAO; Schubert et al. 1993), and vegetation dynamics every 8-days from MODIS (Mu et al., 2007, 2011). The model assumes that biome-specific parameters do not vary spatiotemporally (Running et al.,

2017). Based on the algorithm, MOD16A2 estimates ET at 8-day intervals which has been validated by NASA with measured ET at multiple global eddy covariance towers (Mu et al.,

2011).

4.2.3.4 FAO-56 Kc approach

The Food and Agriculture Organization (FAO) method (Allen et al., 1998) has been used traditionally for ET estimates by multiplying a crop coefficient (Kc) by reference ET. ETref is estimated with the Penman-Monteith equation (Monteith, 1965) for a hypothetical flat, unshaded grass-covered surface with height 0.12 m, albedo of 0.23 and surface resistance of 70

-1 s m at the daily interval. ETref (2010-2017) were collected from the nearest Enviroweather site,

Hastings. Actual daily crop ET (ETcrop) values were calculated by multiplying ETref and the tabulated daily values of the crop coefficients (Kc) for maize (FAO-56; Allen et al., 1998), based on FAO tabulated crop growth stages.

4.2.4 Ensembling approach

As accurate and reliable estimation of ET is of paramount importance for irrigation scheduling (Zhu et al., 2014). A large number of models have been developed for ET estimation.

However, their fidelity depends on the choice of model and associated parameters. Scaling of the potential ET to actual ET requires complex model parameterization and hence the associated uncertainty arises. In this study, we have used the SEBAL energy balance approach (SEBAL), the Priestley-Taylor approach (PT-JPL), and the Penman Monteith approach (MOD16 and FAO-

Kc ) to estimate a more robust actual ET. All of them vary in parameterizations and data requirements. Hence, the actual ET values estimated from these methods are expected to differ.

Moreover, uncertainties in input variables either weaken the model performance or create biased estimation (Ershadi et al., 2014). In order to get the best estimates, multiple models have been evaluated and compared at different locations and time (Xystrakis and Matzarakis, 2010; Fisher et al., 2011, Ershadi et al., 2014; Li et al., 2018; Olioso et al., 2019; Zhang et al., 2020). The ensembling of multiple model outputs not only minimizes the uncertainties in model structures and input but also improves accuracy in output (Vrugt and Robinson, 2007).

Based on ET measurements from the flux towers, all the methods were ensembled through weighted averaging using Inverse Distance Weighting (IDW) method. A weighted average ensemble provides a specific weight to the individual model in a proportion to observed parameter in order to predict that parameter. The ensembling was done with a purpose of achieving more reliable estimates of ET. The general framework is shown in Eqs. 2-4;

푁 푃푗 = ∑푖=1 푊푖 푝푖푗 , ∀푗 (2)

푤푖 푊푖 = 푁 , ∀푖 (3) ∑푖=1 푤푖

퐾 1 푤푖 = ∑푘=1 훽푘 2 , ∀푖, ∀푗 (4) (퐴푝푖푗,푘−퐴표,푘)

where Pj is the ensembled value of ET, Wi is the weight of a ET from an individual method i, pij is ET estimates from method i, j is an index of ET estimates. wi is the inverse squared-distance between the estimated (Apij,k) and observed (Ao,k) ET. N is the number of models selected for ensembling (here, N=4), k is an index for predicted or observed variable(s), K is the number of. variable(s) impacted by pij and 훽푘 is the weight for that variable, k ( Jha et al., 2019, in review).

Furthermore, we also performed a crop stage-based IDW, where ET were averaged at different crop growth stages (Early, developmental, mid and late stages) similar to growth staging in FAO-56 (Allen, 2005). The stage-based averaged ET was used to generate inverse distance Weighting and ensembled further after providing weight to individual method. The calibrated CERES-Maize model in Chapter 4 (Hoogenboom et al., 2013; Jha et al., 2019, in review) was used for designating growth stages and mapped them with FAO-56 categorization.

The early growth stage was designated from the germination of maize to floral initiation (~30 days after planting) when crop growth is minimal. The developmental stage was designated from the floral initiation to 75% silk. The mid stage was designated from the 75% silk to mid of beginning to end of the grain filling period. The late growing stage was designated from the mid of beginning to end of the grain filling period to maturity (Fig 4.4). The end of grain filling is marked by senescence of leaves and formation of black layer in the maize kernels followed by harvest at the moisture level of 15 to 16 % depending on the field conditions and other ancillary supports (Abendroth et al., 2010).

Figure 4. 4. Crop coefficient curve from FAO and the curve created from CERES-Maize growth stages.

4.2.5 Kc Curve and Irrigation Scheduling

The uncertainties associated with ET estimation through an individual method can also be passed on to the derivation of the crop coefficient. In order to obtain reliable estimates, we ensembled ET estimates from SEBAL, PT-JPL, MOD16 and FAO method. The ensembled ET estimates were used to derive crop coefficient using reference evapotranspiration from the nearest weather station, Hastings. The approach and workflow of deriving the crop coefficient in this study is shown in Fig 4.5, where multiple models for ET estimates were ensembled to get

ETRS and later divided by ETref from the nearest weather station to derive the crop coefficient

(Kc).

Figure 4. 5. Conceptualization of developing satellite derived crop coefficient curve.

4.2.6 Development of Kc-curve

The daily ensemble ET estimates were used to calculate the crop coefficient (kc) as Eq.

퐸푇 퐾푐 = 푐푟표푝 (5) 퐸푇푟푒푓

where ETref = reference ET were collected from the nearest weather station which was calculated using Penman- Monteith method with a standard grass (FAO 56, Allen et al., 1998).

The ET estimates from all the methods were ensembled based on crop staging from CERES-

Maize for the study location for eight years (2010-2017). The stage-based Kc curves were generated for each year and for both locations. The curves were fitted based on each growth stages (early, developmental, mid and late) following the FAO-56 approach. Out of the derived daily Kc cloud, the wet days with 5 mm or more rainfall were chosen as a threshold to train the model for picking the candidate-wet days. The best-fit lines were drawn to get Kc- curves for the crop development stages. Eventually all the staged Kc- curves were combined to form a seasonal

Kc- curves for the crop and location. In order to arrive at the best representation of crop coefficients, we averaged kc for all years (2010-2017) and locations (KBS and Marshall Maize farm).

4.2.7 Validation of Kc-curve

The soil water balance model (Martin et al., 1990) was used to validate the derived Kc – curves, which was later used for irrigation scheduling. The soil water balance model is a macro- based automated irrigation scheduling tool, which allows user to approximate growing season length, by inputing planting date, crop and soil type, initial and desired soil available water capacity, and weather input (rainfall, potential ET) (Martin et al., 1990). Based on user selection, it estimates available water in root zone at daily interval, which indicates crop water demand and irrigation requirement. The performance of the soil water balance with standard

FAO Kc- curve and remote sensing derived Kc- curve were compared. Comparisons of simulated available water in the root zone were made with measurements using soil moisture sensors at the Manchester field. The observed volumetric soil moisture were converted as total available water and scaled with root depth factor to derive total available water in root zone using soil water parameters for this texture (Jagtap et al., 2004). We run the soil water balance using three

67 different rainfall datasets, rain gauge in the field, nearest weather station and reanalyzed NASA-

POWER data.

4.2.8 Statistical evaluation of model performance

Model performances were evaluated statistically using coefficient of correlation (R; Eq.

6), Mean Bias Error (MBE; Eq. 7), Root Mean Square Error (RMSE; Eq. 8) and Index of agreement (d-index; Eq. 9) (Willmott, 1982) to measure the models performances,

∑푛 (푂−푂̅)(푀−푀̅) 푅 = 푖=0 (6) 푛 ̅ 2 푛 ̅ 2 √∑푖=0(푂−푂) ∑푖=0(푀−푀)

1 푀퐵퐸 = ∑푛(푀 − 푂) (7) 푛 1

∑푛(푀−푂)2 푅표표푡 푀푒푎푛 푆푞푢푎푟푒 퐸푟푟표푟 = √ 1 (8) 푛

∑푛(푂−푀)2 퐼푛푑푒푥 표푓 퐴푔푟푒푒푚푒푛푡 (푑 − 푖푛푑푒푥) = 1 − ⌈ 1 ⌉ (9) 푛 ̅̅̅ ̅ 2 ∑푖=0(|푀−푂|+|푂−푂|) where M and O are estimated/simulated and observed variables (ET, Total available water in root zone) respectively. These metrics were used to evaluate the performance of ET models and the soil water balance model under standard FAO kc-curve and the derived region specific kc curve.

4.3 Results and Discussion

4.3.1 ET Estimation and comparison

The spatial domain for the remote sensing analysis within the study area covered a large portion of southwestern Lower Michigan (Fig 4.6.) where, a large portion of maize production is irrigated (MSU Extension, 2014). Historically, irrigation transformed the high risk, low water holding capacity soils in this area into productive and profitable croplands. However, over and under irrigation is common among Michigan irrigators, both with economic and ecological consequences. With a goal of improving water use efficiency, we estimated ET from the maize fields in the study area with four methods (SEBAL, PT-JPL, MOD16A2 and FAO-Kc approach) and ensembled them into estimates of field scale ET. Sample ET maps of the early growing season in 2012 based on SEBAL, PT-JPL and MODIS model show the spatiotemporal variation of ET during the early part of the growing season (Fig 4.7).

Figure 4. 6. Bounding box for remote sensing analysis in the study area

Figure 4. 7. ET map (SEBAL, PT-JPL and MOD16A2) for a representative day during growing season (June 26, 2012)

The evapotranspiration for the two maize fields, where flux towers are located, was estimated from eight years (2010-2017) remote sensing data. ET estimates from the individual methods were compared and validated with the measured ET from the eddy covariance towers located in the maize fields. For simplicity, we showed here ET comparison with eddy covariance measurement for one year (2012) at the KBS maize field (Fig 4.8). The ET estimates from all the models were compared for all years (2010-2017) and for both locations.

ET estimates based on Penman – Monteith equation (8-day composite MOD16A2) (Fig

4.8a) were found to have the highest statistical agreement among the individual models (Table

4.1). However, the temporal resolution of 8-day makes it less desirable for decision-making process at the field scale. Some studies have shown that the performance of MOD16A2 remains poor in arid and semiarid regions with significant underestimation of ET (Ramoelo et al., 2014;

Hu et al., 2015). In our study area, under humid continental temperate type climate (Koppen,

1900), the performance was found to be good (Table 4.1), which is in agreement with previous studies (Hu et al., 2015; Yang et al., 2016).

Figure 4. 8. Comparison of estimated ET (a) MOD16A2, (b) SEBAL, (c) PT-JPL, (d) FAO with the eddy covariance measurements at the KBS maize field for 2012.

Table 4. 1. Statistical evaluation of seasonal ET estimates for 2010-2017 among the models and flux tower during the growing season (May- October) at Marshall and KBS maize farms. ET estimates FAO SEBAL PT-JPL MODIS Average Maize Farms KBS Marshall KBS Marshall KBS Marshall KBS Marshall KBS Marshall MBE 0.31 0.30 1.00 0.84 -0.35 -0.54 -0.01 0.01 0.24 0.15 (0.28) (0.32) (0.43) (0.30) (0.40) (0.41) (0.15) (0.17) (0.24) (0.23)

RMSE 0.81 0.68 1.57 1.30 0.45 0.72 0.22 0.23 0.20 0.18 (0.68) (0.56) (.99) (0.55) (0.29) (0.46) (0.13) (0.14) (0.14) (0.13) d-index 0.80 0.87 0.62 0.66 0.80 0.72 0.90 0.91 0.91 0.94 (0.15) (0.09) (0.15) (0.14) (0.08) (0.11) (0.05) (0.03) (0.07) (0.05)

Correlation (r) 0.79 0.81 0.72 0.68 0.75 0.71 0.79 0.78 0.85 0.83 (0.3) (0.31) (0.28) (0.32) (0.30) (0.28) (0.30) (0.30) (0.32) (0.31)

Note: Values in parenthesis are standard deviation for 8 years (2010-2017).

ET estimates from SEBAL were too high in the early and mid-growing season.

Moreover, in the growing season, ET from SEBAL were overestimated with MBE of 1 mm day

-1 and 0.84 mm day-1 for KBS and Marshall Farm respectively compared with measured ET from the eddy covariance (Table 4.1). The most plausible reason behind overestimation lies in its model approach, which estimates ET as residual energy from the energy balance equation

(Eq, 1) and neglects energy stored as body heat in vegetation and the energy required for photosynthesis (Bastiaanssen et al., 1998, 2005). In addition, due to close proximity of the Great

Lakes, residual moisture content at the hot pixels remains high most of the early to mid-growing season which might cause underestimation of the sensible heat flux and hence overestimation of

ET (Fig 4.8 b) (Singh and Irmak, 2011; Singh and Senay, 2016). Early in the growing season, when the canopy is sparse, the heat transfer to soil (G; Eq. 1) becomes part of net radiation if soils are dry (Bastiaanssen et al., 1998). The wet saturated soil in the study area led to underestimation of soil heat flux (G) and hence ET might be overestimated The energy balance models in previous studies have been found to overestimate ET in semi-humid to humid environments with small VPD (Basso and Ritchie, 2018; Yang et al., 2016).

The modified Priestley-Taylor based ET estimates (PT-JPL) were found to have smaller biases and a higher d- index than SEBAL across the growing season (Table 4.1; Fig 4.8c).

Although model performance was good, there are multiple days where considerable deviation from the measured value were observed (Fig 4.8c). The PT-JPL, which generally underestimated ET with MBE of -0.35 mm day-1 and -0.54 mm day-1 for KBS and Marshall

Farm and R2 of 0.75 (KBS) and 0.71(Marshall) (Table 4.1) when evaluated with eddy covariance data, which is in agreement with previous studies under similar climate (Chen et al.,

2013; Ershadi et al., 2013). Moreover, Garcia et al. (2013) compared PT-JPL performance over

73 semi-arid and semi-humid climates and concluded that PT-JPL has lower performance in semi- arid environments than semi-humid.

Traditionally, the FAO- 56 method (Allen et al., 1998) is used for calculating crop water demand and irrigation scheduling using ETref and the crop coefficient (Kc). We performed a single source crop coefficient approach to estimate ET using ETref from the nearest weather station. The overestimation of ET through the FAO method was due to the use of crop coefficient, which did not represent crop and soil moisture conditions at the fields (Er-Raki et al.,

2009). ET estimates from FAO showed high correlation of 0.79 (KBS) and 0.81 (Marshall Farm) and d- index of 0.80 and 0.87 with ET from flux tower. ET estimates were slightly overestimated with MBE of 0.31 mm day-1 (KBS) and 0.30 mm day-1 (Marshall) (Table 4.1). There is a general recommendation that the crop coefficients should be adjusted for specific agro-climatic conditions to better represent stress conditions at the field (Katerji & Rana, 2006).

The daily ET in the months of the maize growing season for all models and inter model comparison with measured ET are shown in Fig. 4.9. The monthly variation in estimating ET within the growing season (May – October) is mostly attributed to the stage of crop growth, development, and crop water demand. Early in the season, crop water requirements remain low and can be met by the combination of precipitation and stored soil water. As the season progresses, increase in VPD regulates water demand (Jha et al., 2018) and irrigation meets this water demand during the peak of vegetative growth stages (June, July and August).

The FAO method has the highest monthly variation (Fig 4.9b) as it utilized an unadjusted, single source crop coefficient (Allen et al., 2016). Higher variation in ET estimates from SEBAL in early season (May) might be due to wet soils and underestimation of H and G

(Eq. 1) (Fig 4.9c). PT-JPL underestimated ET (Fig 4.9a) as compared to measured ET from the eddy covariance data for all months in the growing season (Fig 4.9a). However, all model captured the seasonal ET trends for maize.

(a)

(b)

Figure 4. 10. Temporal variation of seasonal ET among the models and ground observations from flux towers during growing season (May- October) at KBS (a) and Marshall (b)

Figure 4. 11. Inter model comparison of seasonal ET among the models and ground observations from the flux tower during growing season (May- October) at KBS

Figure 4. 12. Inter model comparison of seasonal ET among the models and ground observations from the flux tower during growing season (May- October) at Marshall

Moreover, the interannual varaition in ET for both the locations and all models are shown in Fig. 4.10. Intermodel comparison across the years (2010- 2017) (Fig. 4.11, 4.12), show that MODIS ET data follow the pattern of the measured ET from the eddy covariance.

Seasonal ET estimates from PT-JPL were lowest and from SEBAL were highest among all the models and across all the years (Fig 4.11 and 4.12). The range of seasonal ET estimates were highest in the FAO approach. Each of the model were found to have their own advantages and limitations leading to uncertainties and biases in ET estimation.

4.3.2 Ensembling ET estimates

The stage based ensemble ET (S-IDW) were estimated for all years and locations. The length of crop development stages simulated by CERES-Maize model are shown in Table 4.2.

These ET from all the individual methods were averaged during these stages to estimate the weighted average ET estimates. The representative ensemble ET (2012- KBS) is shown in Fig

4.13. The comparison of monthly average of ensemble ET with flux-tower data (2010-2017) showed RMSE of 0.2 and 0.18 mm day-1, and 0.91 and 0.94 d-index for KBS and Marshall

Farm respectively (Table 4.1)

Table 4. 2. DSSAT based simulated and updated length of crop development stages used for ensembling purposes Length of crop development stages Year Early (DAP) Developmental (DAP) Mid (DAP) Late (DAP) 2010 0-31 32-69 70-105 106-136 2011 0-32 33-68 69-108 109-138 2012 0-33 34-64 65-97 98-132 2013 0-37 0-80 81-112 113-142 2014 0-33 34-79 80-109 110-138 2015 0-34 35-71 72-107 108-140 2016 0-34 35-71 73-104 105-133 2017 0-35 36-76 77-114 115-148 Note: DAP- Day after planting

Figure 4. 13. Ensemble ET estimates by stage based Inverse Distance Weighting (IDW) for 2012 with a reference of ground observations from the flux tower during growing season (May- October) at KBS.

Mean of monthly ET estimates from the individual and ensemble methods were compared with ET from the flux towers at both the locations (Fig. 4.14, 4.15). ET estimates from

FAO were found to be the highest followed by SEBAL ET across the years (2010-2017). ET

MODIS were found to the closest with the observed one throughout this study period. ET from

PT-JPL were found to be underestimated. However, ensemble ET (average) (Fig 4. 14, 4.15) performance was best among all the methods with d-index of 0.91 and 0.94 at KBS and Marshall field (Table 4.1). The daily ET estimate from ensemble method (2010-2017) for both locations are shown in Fig. 4.16 and 4.17. The median value of daily box plots for eight years could capture the seasonal ET trend from the measured ET (Fig 4.17, 4.18 and 9a). The ensembled ET in the season were in agreement with maize seasonal crop water demand as physiologically simulated by 29 maize models (Kimball et al., 2019) and for the humid temperate climate conditions similar to study area (Abraha et al., 2016; 2019).

Figure 4. 16. Variation in ensembled daily ET estimates for crop season (May- October) for 2010-2017 at KBS

Figure 4. 17. Variation in ensembled daily ET estimates for crop season (May- October) for 2010-2017 at Marshall

4.3.3 Development of Kc – Curve

The distribution of Kc derived from the ensemble ET estimates are shown in Fig 4.19 and 4.20 for the respective KBS and Marshall maize fields for eight years (2010-2017). The temporal variation in Kc during the early season was due to the varying soil wetness and soil evaporation. As canopy cover increased during the peak growing season, Kc was mostly influenced by the transpiration. In 2012 (a dry year), the Kc values for both the locations were lower than the standard FAO-Kc, throughout the season. The negative deviation from the standard FAO- Kc might be due to scant rainfall during development stage, as ET in that case is limited by the energy availability (Tasumi et al., 2005). Except for the early season, the derived crop coefficients were lower than standard FAO Kc-curve in the growing season. The positive deviation from the standard FAO- Kc can be attributed to the rainfall events. In the irrigated field, a more positive deviation can be observed during irrigation events (Tasumi et al., 2005).

These deviated Kc-curves were fitted according to different development stages (early, developmental, mid and late) similar to the FAO standard. The candidate-wet days were chosen based on the rainfall threshold value (≥5 mm). The chosen Kc values for the wet days were fitted to a trend line.The fitted curves were generated for all the years and for both locations by aggregating individual fit stages into a seasonal curve (Fig 4.21). The reason for temporal variation in Kc- curve is attributed to our approach of derivation, where we used calibrated

CERES-Maize for defining the length of the growth stages (Fig 4.4; Table 4.2). The study area contained two different maize fields with different land-use histories (Section 2.1).

Figure 4. 18. Temporal variation in crop coefficient curve of Maize derived by ensemble ET for 2010-2017 at KBS

Figure 4. 19. Temporal variation in crop coefficient curve of Maize derived by ensemble ET for 2010-2017 at Marshall

Figure 4. 20. Smooth fitted crop coefficient curve of Maize derived by ensemble ET for 2010-2017 at KBS and Marshall

The Kc curves were averaged temporally (2010-2017) for both the locations, with a goal to nullify the effect of temporal variation in the Kc (i.e. interannual variablility) and resulting into the mean state of the Kc (i.e. intraseasonal pattern). Furthermore, the average Kc- curve from the two locations were combined. This was done to introduce the effect of two different replicates of the maize fields representing two different land-use histories.

Figure 4. 21. Averaged (2010-2017) crop coefficient curve of Maize at KBS and Marshall

Additionally, we compared the standard FAO Kc – curve with the consolidated Kc – curve for KBS, Marshall’s Farm and the average of both the locations (Fig 4.22). All four of the

Kc – curves were validated using soil water balance model. Across the year, KBS maize farm had the lowest crop coefficient curves among all the curves. This field was under 50-year corn- soybean rotation before 2010 and hence due to loose soil structure and poor water storage capability in the root zone, might be one of the reason for the low Kc (Abraha et al, 2016).

Moreover, Marshall was under grassland for 50 years before 2010 and likely has a higher water

89 storage capacity in the root zone. However, the temporal stability of soil moisture across long- term agricultural system experiments is a current area of investigation (Abraha et al., 2016; Fry et al., 2018; Zhao et al., 2018) and out of the scope of this study.

4.3.4 Validation of Kc – Curve

The volumetric soil moistures were converted to total available water in root zone using soil water parameters (Jagtap et al., 2004) (Fig 4.23b). The point values of crop coefficient curve were fed to the model at the different percent of growth, and model interpolated Kc curve from the point values.

Table 4. 3. Crop coefficients as an input for soil water balance model Percent of Growth Root Depth Maize Crop Coefficients (Kc) (%) (cm) FAO KBS Marshall Average 0 15.2 0.30 0.45 0.43 0.44 10 32.2 0.30 0.45 0.43 0.44 20 49.1 0.30 0.45 0.43 0.44 30 66.0 0.50 0.54 0.56 0.55 40 83.0 0.85 0.70 0.78 0.74 50 91.4 1.20 0.89 1.04 0.97 60 91.4 1.20 0.89 1.04 0.97 70 91.4 1.02 0.89 1.04 0.97 80 91.4 1.08 0.89 1.04 0.97 90 91.4 0.77 0.63 0.68 0.66 100 91.4 0.46 0.38 0.46 0.42

The grower recorded the amount of irrigation applied. We found that the rain gauge in the field had some erroneous results, particularly during heavy rainfall events, due to clogging by insects and dust particles. Hence, with the three rainfall datasets (Rain gauge, Enviroweather and NASA-POWER), the soil water balance model was run to estimate the total available water

(TAW) in root zone (Fig 4.24 a, 4.24b, 4.24c). We compared simulated TAW with observed

TAW to evaluate the model performances.

Table 4. 4. Statistical evaluation of soil water balance model performance under three different rainfall inputs by comparing observed and simulated total available water in root zone (mm).

Rainfall Input Kc-type MBE RMSE d-index Correlation (R) Rain Gauge FAO-Kc -4.21 19.11 0.54 0.33 (At field) Kc (KBS) -0.35 14.75 0.66 0.45 Kc (Marshall) -2.56 17.00 0.60 0.39 Kc (Average) -1.48 15.85 0.63 0.42 Weather Station FAO-Kc -2.15 13.65 0.67 0.46 (Enviroweather) Kc (KBS) 2.46 12.29 0.70 0.48 Kc (Marshall) -0.65 11.87 0.74 0.55 Kc (Average) 0.64 11.55 0.74 0.55 NASA-POWER FAO-Kc 2.22 10.80 0.76 0.58 Kc (KBS) 3.90 11.20 0.77 0.62 Kc (Marshall) 2.86 10.93 0.77 0.61 Kc (Average) 3.37 11.04 0.77 0.61

The derived Kc performed better than the standard FAO in all cases. However, overall performance of the model was best when reanalysed rainfall values from NASA-POWER were used (Kc- average; d-index - 0.77; R - 0.61) (Table 4.4). We used the validated water balance model for irrigation scheduling at Manchester maize field and compared it with farmers’ practice of irrigation (Fig 4.25 and 4.26).

(a) (b)

Figure 4. 22. Observed rainfall and irrigation amount with (a) volumetric soil moisture at different depth (15, 30, 45, 60 and 90cm); (b) total available water in root zone (90 cm)

Figure 4. 23. Observed and simulated total available water (TAW) in root zone with three rainfall input (a) rain gauge (b) weather station (c) reanalyzed product from NASA-POWER, and irrigation value reported by grower at Manchester maize field for four Kc- curve (FAO, KBS, Marshall, and average) in 2019 maize growing season.

4.3.5 Irrigation Scheduling of Maize: a comparison of standard FAO- Kc and the derived Kc

Irrigation scheduling tools, using the concept of reference evapotranspiration and the crop coefficients were initiated by Jensen (1969) and have been continuously evolving. The irrigation- scheduling tool (Martin et al., 1990) used in this study uses the variables: ETref and Kc, along with observed rainfall to estimate total available water in the root zone and allows user to irrigate when the available soil water in root zone reaches the threshold of percentage capacity filled. We set up 60% threshold for our study and irrigated on the days when available soil water in root zone crosses/nearly-crosses the 60% capacity (Fig 4.25a, 4.25b). The available water holding capacity of the soil (cm water/cm of soil) for our simulation is listed in table 4.5.

Table 4. 5. Available water holding capacity of soil and percent capacity filled at different depth for the sandy clay loam (Jagtap et al., 2004)

Depth Range (cm) AW (cm/cm) Capacity filled (%) 0-15 0.2286 60 15-30 0.2286 80 30-45 0.2286 95 45-60 0.2286 95 60-90 0.2286 95

We compared two crop coefficients (FAO Kc and derived Kc (average)) to simulate our irrigation scheduling output and found that total irrigation added in the season using FAO - Kc was 214 mm whereas using our derived Kc, total irrigation applied was 153 mm in a growing season of 2019. We also compared the results with the irrigation values that farmer had reported and ran our irrigation scheduling tools to investigate his amount and timing of irrigation. We found that although the farmer applied exactly the same amount of 214 mm as simulated by our tool using FAO Kc but the timing was more frequent during post silking and early grain filling periods (Fig. 4.26) and most of them went to drainage. It can be noted that the field also experienced water stress during late grain filling and maturity period after August 27, 2019 (Fig.

4.26). The results of our comparisons suggest that farmers can minimize the irrigation applications and maximize profitability, if they use our derived crop coefficients. When the FAO

Kc and derived Kc were compared using the irrigation-scheduling tool, the difference in irrigation amount was found to be 60 mm (2.4 inch). And, for 100-hectare (247 acre) farm, it is equivalent to 70 million litre (16.1 million gallon) excess water in a season, leading to economic and environmental losses.

4.4 Summary and Conclusions

Improved water management for a crop requires an understanding of daily to seasonal water dynamics in soil-plant-atmospheric continuum. Crop evapotranspiration plays a significant role in the demand side of this flux and accurate estimation of crop evapotranspiration for water management or irrigation scheduling is key. Traditionally, irrigation scheduling has been done

96 by estimating crop water demand from the crop coefficients and reference evapotranspiration.

However, spatiotemporal variability in soil and crop type, management practices and other physiographic factors influence the crop coefficients. Characterization of the crop coefficients through remotely sensed vegetation indices or inversely through ET estimate from models have gained momentum in the past few decades. Each individual method has its own advantages and limitations related to uncertainty in ET estimation. In this study, we have ensembled multiple ET estimation methods to get reliable ET estimates through a physiologically crop stage-based

(derived by crop model) Inverse Distance Weighting (IDW).

SEBAL tends to overestimate ET across the entire growing season, whereas PT-JPL tends to underestimate. MOD16 ET estimates were closest to the observed variation of ET. We found that ensembled ET method outperformed all individual methods with up to 94% agreement (d-index) and 85% correlation (r) with the measured ET. We used ensembled ET to derive crop coefficient Kc- curve and validated those using the water balance model. Kc derived from our study performed better than the standard FAO – Kc. Using the derived Kc, we designed irrigation scheduling for maize field at Manchester, Michigan where we have installed soil moisture at different depth. Our simulated soil water availability in the root zone (90 cm) were in agreement (60 %) with the measured soil water balance in root zone. Based on the results of this study we can draw the following conclusions:

• The accurate ET estimate from ground based sensors like, eddy covariance aids in water

management.

• Remote sensing aids in ET estimation across spatio-temporal scales in data scarce

environments.

• The energy balance model, SEBAL overestimates ET most days in the season. However,

MODIS and PT-JPL underestimates, creating uncertainty in ET estimates.

• Ensembling of ET estimates minimizes the uncertainty associated with individual model.

• Crop models can be used in simulating the growth stages which guides stage based

ensembling using Inverse Distance Weighting method based on ET measurements from the

eddy covariance.

• Ensembled ET derives crop coefficients that are more site specific than standard FAO kc.

• Derived region/site-specific kc used in soil water balance for irrigation scheduling can save

water thus improving profitability and environmental stewardship.

4.5 Acknowledgements

This work is partly funded by project GREEEN (Generating Research and Extension to meet Economic and Environmental Needs) with AgBioResearch, MSU Extension, and the

Michigan Department of Agriculture and Rural Development (MDARD). We acknowledge

Chubu University, ListenField, USAID-Philippines and NASA-SERVIR for partly funding

Prakash Jha’s research work at MSU.

5. DOWNSCALING SEASONAL RAINFALL AND TEMPERATURE FORECASTS TO

DEVELOP A RISK ANALYSIS MODEL FOR EAR ROT DISEASE MANAGEMENT

IN MAIZE

5.1 Introduction

The global crop production system remains vulnerable to climate variability despite technological and varietal improvements. Specifically, the variability in climatic extremes has increased over the past few decades and made food production systems more sensitive and vulnerable to them, especially those under rainfed conditions (Lobell et al., 2008; Hatfield et al.,

2011; Kistner et al., 2018; Ogutu et al., 2018). In order to meet the challenges posed by climatic variability, tools for informing in advance associated decisions and preparedness can help growers optimize agronomic management and use of their resources. The availability of climate information within the growing season is relevant in agricultural risk management (Hansen et al.,

2006). Yield prediction under variable and extreme events can provide an opportunity to policy makers to prepare and assist stakeholders develop preemptive actions (Shelia et al., 2019).

However, the usefulness of such information among farming communities varies with the timing and lead time of that information and the extent of farmers’ adaptability (Haigh et al., 2015).

In the Midwest US, maize (Zea mays L.) is a major crop, producing 14.6 billion bushels from an acreage of 90.2 million acres (USDA-NASS, 2017). Michigan is in the northeast part of the Corn Belt, growing maize mostly under rainfed conditions, with an average yield of 161 bushels per acre as compared to national average of 176 bushel per acre (USDA-NASS, 2017).

With a goal of achieving higher yield, there have been continuous improvements in genetics and management in the last few decades (Sacks and Kucharik, 2011). In spite of these improvements, maize is sensitive to seasonal climatic variability at different stages of development. These

99 sensitivities ultimately have an effect on yields. Due to highly incentivized maize prices, grower’s focus have shifted to selecting high yielding hybrids rather than disease resistant varieties. Combined with continuous production of maize with minimum tillage and erratic weather patterns, conditions became more suitable for fungal diseases outbreaks (Wise and

Mueller, 2011). In Michigan, Gibberella and Fusarium, which produce mycotoxins, are the primarily causal organisms for the development of ear rot disease in maize (Chilvers, 2018). The pathogen spreads through infecting maize ears during early silking stage, approximately 6 to 8 days after silk emergence (Reid et al., 1992; Warfield and Davis, 1996; Munkvold, 2003;

Schmale III and Bergstrom, 2004). The ear rot in maize causes quantitative and qualitative losses through producing mycotoxins, like deoxynivalenol (DON; Pitt and Miller, 2016; Mueller et al.,

2016).

Weather, being an important driver of disease development (Chilvers, 2019), favorable condition during anthesis, helps in its spread. However, there is a debate among maize growers and the scientific community whether infection occurs under environmental conditions varying from cool and wet (Blaine and Singh, 2018; Wise et al., 2016; Durst and Singh, 2019), with moderate temperature and wet conditions (Chilvers, 2019) to hot and dry conditions (Kaatz,

2018. MSU extension; Crop protection network, 2019). Specifically, favorable weather conditions for infection are reported to be at high humidity (≥ 80%) and temperature ≥ 24ᵒC

(Sutton, 1982; Miller, 1994; Vigier et al., 2001; Munkvold, 2003; Mansfield et al., 2005).

Ambient temperature and moisture are suggested to be the two important indicators of risk for ear rot disease. Vigier et al. (2001) also conducted regression analysis of DON and humidity and found that high humidity (≥ 80%) during July to September is favorable for DON development.

Mansfield et al. (2005) evaluated the correlation of weather variable(s) (daily average

100 temperature and humidity) with disease development during tasseling to maturity and found that

DON levels and average daily temperatures were positively correlated during tasseling, silking and milk stages, and negatively correlated with daily precipitation during the blister stage.

Studies have also found that cool and wet conditions during grain filling and maturity phase promote the development of DON. The correlations of DON and weather variables are bound to the critical crop growth stages, which are controlled by temperature during the growing season

(Ghamghami et al., 2019). Variability in temperature and rainfall during the growing season makes management decisions more challenging due to associated variability in the crop growth stages.

Although an expensive option, the incidence of ear rot can be minimized by applying fungicide (Chilvers, 2018). However, if any preemptive actions are supplemented by predictions of climate/weather and associated crop growth stages during silking to maturity, it can better inform growers to improve the efficacy of their fungicide applications or divest from using fungicide depending on the levels of predicted risks (Anderson et al., 2017; Rosburg and

Menapace, 2018). These critical growth stages and associated management strategies can be predicted and evaluated using process-based crop models (Ritchie and Nesmith, 1991; Rodríguez et al., 2019).

Advanced climate information plays a significant role in the process of decision making at the earlier crop development stages and lets growers prepare for preemptive actions (Hansen et al., 2006). Unlike deterministic (weather scale) predictions, probabilistic seasonal climate predictions usually have low-moderate skills but have an advantage in terms of greater lead- times. This lead-time advantage can help growers in managing farm related decisions effectively and prepare farm management logistics in advance (Meinke et al., 2004). However, because of

101 the disparity between the temporal scale of data produced (i.e., three months) and the scale of grower’s decisions (i.e., tactical and strategic), the use of seasonal climate forecast (SCF) is not always straightforward (Hansen et al., 2006). In addition, climate service providers provide SCF in the form of tercile probabilities e.g., probabilities of above normal (AN), near normal (NN) and below normal (BN). With advances in climate applications, this forecast information can be downscaled at the daily basis to inform process-based crop models simulate phenology and yield

(e.g., Hansen and Indeje, 2004; Hansen et al., 2006; Apipattanavis et al., 2010; Shafiee-Jood et al., 2014; Kim et al., 2016; Han et al., 2017).

There are several downscaling tools, which can downscale rainfall from the SCF and link the outputs with crop models (e.g., Hansen and Indeje, 2004; Semenov and Doblas-Reyes, 2007;

Capa-Morocho et al., 2016; Han and Ines, 2017; Ines et al., 2018; Han et al., 2019, among others). However, there is a lack of downscaling methods, which can downscale probabilistic rainfall and temperature forecasts from the SCF and link with the crop models (Ines et al., 2018).

For maize disease management, temperature is critical to better predict phenology. Currently,

Midwest maize growers use U2U Corn GDD tool to track crop growth and make important decisions prior and during the growing season (Angel et al., 2017; Prokopy et al., 2017).

However, the tool only uses historical and forecast temperature (30- days) and hence cannot predict silking with a lead-time of more than one month. Downscaled temperature and rainfall forecast from SCF when linked with process based physiological crop models can predict phenology with a lead-time of more than a month.

The overall goal of this study is to evaluate the performance of downscaling rainfall and temperature in the development of a risk analysis model for ear rot management of maize in

Michigan. Specific objectives are: i) to downscale seasonal probabilistic rainfall (P) and

102 temperature (T) forecasts for predicting phenology, ii) to develop a risk analysis model for ear rot management in maize, and iii) to validate the risk analysis model at selected locations in

Michigan.

5.2 Materials and Method

5.2.1 Study Area

The study area consists of field trial locations from Michigan Maize Performance Trial program (MMPT; Fig. 5.1). The county (city) locations selected for this study were Saginaw

(New Lothrop; 43.13ᵒ N, 83.97ᵒ W), Huron (Bad Axe; 43.83ᵒ N, 82.98ᵒ W) and Montcalm

(Greenville; 43.22ᵒ N, 85.21ᵒ W). The risk analysis was conducted in 2017 and 2018 based on disease severity recorded at those three locations (Blaine and Singh, 2018).

Figure 5. 1. Study area having high ear rot disease severity in 2018.

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5.2.2 Data collection

5.2.2.1 Weather and climate data

5.2.2.1.1 Observed weather data, 2017-2018

The weather data for this study (Saginaw, Huron and Montcalm) were obtained from

Enviroweather network, a sustainable weather-based information system that helps growers and stakeholders in making farm related decisions in Michigan. The nearest weather station to

Saginaw is Flint at Applewood estate (43.02 N, 83.68 W, 229 m asl), to Huron is Kinde at

Meade piling ground (43.92 N, 83.01 W, 216 m asl) and to Montcalm is Entrica at MSU

Montcalm Research farm (43.35 N, 85.18 W, 290 m asl).

Figure 5. 2. Observed weather variables for 2017 and 2018 during crop growing season in the study area.

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The daily rainfall, average relative humidity and average temperature data were collected for 2017 and 2018 (Fig. 5.2). Across all locations, the average temperature were higher in July and Augusts in 2018 than 2017 and rainfall was higher (up to 20 mm in a day) in late August and early September in 2018 as compared to 2017 (up to 10 mm in a day), which might contributed to the DON development (Fig. 5.2).

5.2.2.1.2 Historical weather data

The historical climate data (1981-2010) were obtained from the nearest weather stations of Enviroweather network (https://enviroweather.msu.edu/), and Global Historical Climatology

Network (GHCN) Daily Database (Menne et al., 2012). The missing weather data (especially relative humidity) were collected from the reanalysed data from the NASA-POWER, which is available at 0.5 X 0.5 degree grid. The climatology (1981-2010) of relative humidity, average temperature and total rainfall for the months of interest for all three locations are shown in Table

5.1.

Table 5. 1. Monthly averages of weather variables (1981-2010) in the study area. Month Rainfall (mm) Average Temperature (ᵒC) Relative Humidity (%) S H M S H M S H M May 87.9 79.9 92.9 13.2 10.1 13.1 72.1 78.7 73.4 Jun 87.3 82.0 88.5 18.4 15.9 18.3 73.2 78.4 74.2 Jul 83.1 78.3 78.4 21.0 20.1 20.8 72.1 76.5 72.6 Aug 85.7 81.4 88.9 20.6 20.3 20.4 72.6 76.1 72.4 Sep 78.1 78.9 79.7 17.0 17.0 16.6 71.9 75.4 71.9 Oct 80.2 81.0 96.2 10.1 10.7 9.8 74.8 77.1 75.9 Note: S- Saginaw; H- Huron; M- Montcalm

5.2.2.1.3 Seasonal climate forecast (SCF) data

The seasonal climate forecast data were obtained from the NOAA’s archived gridded forecast data, which was stored in GRIdded Binary or General Regularly-distributed Information in Binary form (GRIB2) format at National Digital Forecast Database (NDFD) server (Glahn and

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Ruth 2003, Ruth et al., 2009). These digital forecasts are prepared and produced in collaboration with Weather Forecast Offices (WFOs), National Centers for Environmental Prediction (NCEP) and the Central Quality Control unit at National Weather Services (NWS) and verified through a prototype for point-to-point comparison of temperature and rainfall forecast to the observation

(Dagostaro et al., 2004; Ruth et al., 2009). The data files are organized by headers designed by

World Meteorological organization (WMO) and categorized by issuance by time of the day. The study area is in the grid of Central Great Lakes region according to the database of NDFD. The forecast data for 2018 were decoded using GRIB2 decoder (Zeng, 2018). In this study, we used forecasts for June-July-August (JJA) since anthesis and associated ear rot disease incidence coincide mostly during the last week of June to 4th week of July, depending on planting date.

The overarching goal of using SCF is to provide the users a time horizon where they can decide how to incorporate the forecast meaningfully into farm related decisions (Fig. 5.3). The historical datasets were used to predict the climatological ranges of phenology to compare with the observed timing of phenology. Once the crop model (CERES-Maize) is calibrated, the historical observed data can be utilized for regression-based decision making involving the past data. Moreover, the growers can use SCF for farm related decisions, if these tailored climate information are placed in a usable form in the grower’s hand. SCF for May-June-July (MJJ) can be used in April to decide the planting date based on the predicted weather conditions. The forecast data for June-July-August (JJA) is important for tactical (in-season) decision making at the farm, which involves decision related to irrigation, fertilizer and pesticides application

(fungicides applications in this study). The forecast for the later part of the season can be used for post-harvest processing and storage decisions. For example, in ear rot disease management, designing grain moisture management strategies for minimizing the risk of DON can be done by

106 drying seeds after harvest and in storage, as DON concentration is directly proportional to moisture content in kernels (Munkvold, 2014).

(a)

(b)

Figure 5. 3. Illustration of (a) historical and climate prediction utility for management decisions (b) prediction horizon of seasonal climate forecast and usability for farm related decisions.

5.2.2.2 Ear rot incidence and mycotoxin data

Data on ear rot disease incidence and severity in 2017 and 2018 were collected at each location in Fig. 5.1. The severity of ear rot was calculated as the number of damaged kernels on each damaged ear. An index for ear rot (ERI) was calculated as the product of incidence and severity (Groth et al., 1999). DON data at each location were analyzed from ground samples through 1 mm screen using gas chromatography- mass spectrometry (GC-MS) at the Mycotoxin

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Diagnostic Laboratory, University of Minnesota. The Risk Analysis Model evaluates only the ear rot disease risk in Saginaw, Huron and Montcalm (Fig. 5.1).

5.2.2.3 Anthesis data

Field observation data on anthesis (75% of silk) were collected at each location during period of fungicide (proline) application. These data were compared with estimated data on silking based on accumulation of growing degree days (GDD) using Useful to Usable tool

(Angel et al., 2017) (Table 5.2). The GDD are calculated using standard formula (Eq. 1).

(푇 + 푇 ) 퐺퐷퐷 = ( 푚푎푥 푚푖푛 − 푇 ) , (1) 2 푏푎푠푒

where Tmax is the daily maximum temperature up to 86 ᵒF (30 ᵒC) and Tmin is the daily minimum temperature up to 50 ᵒF (10 ᵒC) and Tbase is the lowest temperature for maize optimum growth 50

ᵒF (10 ᵒC). The range is based on the optimum temperature range for maize growth, which is 50

ᵒF to 86 ᵒF (10 ᵒC to 30 ᵒC).

The projection of Maize GDD for next 30 days are based on operational ensemble forecast from National Weather Service (NWS) (Saha et al., 2014) and the progression of phenological stages are counted based on GDD accumulation explained by Abendroth et al.

(2011). Silking can be estimated based on a simple regression (Eq. 2), which varies with the different hybrid seed companies;

퐺퐷퐷푆푖푙푘푖푛푔 = 192.8 + 10.66(퐶푅푀), (2)

where, GDD = Growing Degree Days (ᵒF) CRM = Comparative Relative Maturity rating reported on hybrid maize bags (in days)

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Table 5. 2. Field observation and U2U estimation of silking stage at Saginaw, Huron and Montcalm in 2018.

Locations Planting Anthesis (75% silking) Silking

date Field observation (DAP) (U2U) (DAP) Saginaw May 30 65 65

Huron May 16 72 75 Montcalm May 18 64 67

5.2.3 Downscaling rainfall and temperature: software description

Recently, temporal downscaling techniques were developed to disaggregate probabilistic seasonal precipitation (P) outlooks using resampling (FResampler1) and conditional weather generators (predictWTD) (Han and Ines, 2017; Han et al., 2017). The FResampler1 is based on the concept of ‘conditional block sampling’ of weather data conditioned on the probabilities of forecasts. This method randomly samples a block of daily time-series of weather data for a target season from historical observations conditioned on those tercile probabilities. Sampling is done with replacement. FResampler1 preserves the covariance between rainfall and other weather variables, e.g., minimum and maximum temperature and solar radiation on a particular day (Han and Ines, 2017).

The tercile probabilities of rainfall (P) and temperature (T) predictions from SCF were downscaled simultaneously using “FResamplerPT”, an innovative downscaling tool (Ines et al.,

2018). The goal of downscaling P & T simultaneously is to produce realizations of P’s and T’s whose union probabilities will preserve their forecast probabilities (Fig. 5.4). It aims to find intermediate probabilities p (P’) and p (T’) that will be used to sample P, then T, such that when the union of both realizations is taken, the resulting p (P’) ≈ p (P) and p (T’) ≈ p (T) (Ines et al.,

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2018). Since we are developing a risk analysis model for ear rot management, we included relative humidity (RH) in the pool of weather variables when P&T are being downscaled.

Using this downscaling tool, the NOAA three month SCF of June-July-August (JJA) in

2018 were downscaled to generate daily weather variables (i.e., rainfall, minimum and maximum temperature, solar radiation and relative humidity). The long term historical weather data (1981-

2010) were used as climatology for analysis. The 2018 probabilities for rainfall (42 AN : 34 NN :

24 BN) and temperature (33 AN : 33 NN : 34 BN) for the Central Great Lakes regions were used for this study. The purpose of using JJA in this study was to match the anthesis and ear rot incidence period in maize growth and development. For a normal planting window of May 1 –

15, tasseling to silking phase could vary from June 25- July 15 depending on weather conditions and hybrid selection (Abendroth et al., 2010). Moreover, we performed downscaling of four hypothetical extreme forecast scenarios to compare behaviours of the risk analysis tool based on

110 possible weather combinations favourable for disease develeopment. The scenarios were; warm and humid forecast (50 AN : 33 AN: 17 BN for rainfall and temperature), the warm and dry forecast (7 AN : 33 AN: 60 BN for rainfall and 60 AN : 33 AN: 7 BN for temperature), the cool and wet forecast (60 AN : 33 AN: 7 BN for rainfall and 7 AN : 33 AN: 60 BN for temperature), and the cool and dry forecast (7 AN : 33 AN: 50 BN for rainfall and temperature).

5.2.4 Crop model setup

We used the calibrated DSSAT CERES-Maize in Chapter 1. The SCF was disaggregated into seasonal long term weather data (.WTD) files and converted to yearly weather (.WTH) files as input weather files for DSSAT. We generated 200 realizations (100 P and 100 T) for each forecast scenarios, i.e., 200 different weather files for each location. Experimental files with 200 treatments and fields were created for each of location (Saginaw, Huron and Montcalm), which used the 200 different weather realizations (each one is assumed to be a weather file for each assigned field in that location). Outputs of the 200 treatments were used to predict anthesis/silking, which was used as a basis in developing the time horizon of the ear rot risk analysis model.

5.2.5 Developing risk indicators

Based on previous studies of similar climatic zones (Mansfield et al., 2005; Munkvold,

2014), the follwing thresholds of weather variables were selected: Temperature ≥ 24ᵒ C; Rainfall

≥ 1 mm; Relative Humidity ≥ 80% during July and August. The tasseling, silking and milking stages are highly correlated with ear rot incidence that fall in the months of July and August

(approximately based on May/June planting period). The daily weather variables were assigned a unique flag to identify the days when it crossed the threshold using a simple logic model (e.g.,

Shah et al., 2019). In the risk analysis model, we subject an “IF” logic to the variables. If the

111 condition is met, it returns a flag as a risky day. We also performed another logic function “IF-

AND”, where the combinations of two variables meet the set conditions, and that day is counted as a risky day. We developed risk indicators for temperature (T), rainfall (R) and relative humidity (RH) individually with “IF” logic, and then developed combined risks for T and RH and T and R with boolean “IF-AND” logic. Using the prediction of phenology in Section 5.2.4, one can hone in more on detailed risks.

The daily risk factors were converted into risk probabilities based on the 200 realizations for the forecast, and from 30 years observations for the climatology. The daily risk probabilities were averaged at five-day intervals in the time series to better match the requiurements of disease progression monitoring (Mansfield et al., 2005). We analyzed and validated risk indicators using disease severity in 2018. Data from 2017 was also used to analyze the reason for lower DON levels during that growing season.

5.2.6 Statistical analysis

The 5-day average risk probabilities led to 12 sets of different risk probabilities for climatology and forecast, respectively. The 12 sets of climatological of risk probabilities were compared with the 12 sets of forecasted risk probabilities for each variable (T/P/RH) and the combinations (T&RH; T&P). In order to test the significance, t-tests were conducted by bootstrapping the data until 500 replicates (see Hall and Wilson, 1991), using T-Score Calculator

(https://www.socscistatistics.com/pvalues/tdistribution.aspx). The p-values were estimated for each variable and their combinations, at 5% and 1% significance levels.

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5.3 Results and Discussions

5.3.1 Predicting phenology

The forecast for 2018 and hypothetical extreme forecast scenarios were downscaled using

FResamplerPT and 200 realizations were used to simulate crop growth stages using CERES-

Maize model in the DSSAT. The predicted anthesis (box-plots) for all five-forecast categories

(2018; warm and humid; warm and dry; cool and wet; and cool and dry) are shown in Fig. 5.5.

The purpose of predicting phenology was to evaluate risk indicators during anthesis from the risk analysis model.

At Saginaw, the mean value of predicted anthesis using NOAA forecast is the same as the observed anthesis in the field (Fig. 5.5a). However, the anthesis were over predicted by four and three days at Huron and Montcalm, respectively (Fig. 5.5.a). Physiologically, the more heat accumulation in plants due to warm conditions will accelerate the phenological development compared to cooler conditions (e.g., Zhu and Troy, 2018). This observation was found to be true for all forecast scenarios (including actual 2018 forecast) at each location. In Saginaw, the mean value of anthesis among 200 realizations were 63 days after planting (DAP) for both the warm and humid, and warm and dry forecast, which were two days earlier than the observed anthesis

(65 DAP) (Fig. 5.5b). However, cool conditions with slower heat accumulation showed over prediction of anthesis (Fig. 5.5d-e). Specifically, the mean values of predicted anthesis were one day higher (66 DAP) than observed anthesis (65 DAP) at Saginaw, four days higher (76 DAP) than observed (72 DAP) at Huron, and four days higher (68 DAP) than observed (64 DAP) at

Montcalm. A similar trend was observed between Huron and Montcalm. Due to its vicinity from the Great Lakes, changes in rainfall and temperature might have significant contributions to the variations in predicting anthesis at Huron, in all forecast scenarios (Fig. 5.5d, 5.5e, 5.5f).

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Figure 5. 5. Predicted anthesis for all three locations for 2018 forecast (a), warm and humid forecast (b), warm and dry forecast (c), cool and wet forecast (d), and cool and dry forecast (e).

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5.3.2 Risk Probabilities using Forecast data

The developed risk probabilities for all the individual variables, and combinations of variables for all forecast scenarios are shown in Fig. 5.6 - 5.12.

The risk probabilities of exceeding the temperature threshold at Saginaw indicated a favorable weather condition for disease development during 3rd week of July and it might coincide with anthesis, if maize is planted during the normal planting window of May 1-15.

However, due to the delay in planting (May 30), anthesis were observed on August 3 (65 DAP).

The downscaled weather from forecast of 2018 captured the trend of risk probabilities for temperature and was slightly higher than climatology (Fig. 5.6a). The risk probabilities exceeding the temperature threshold for Huron was higher than the climatology during the entire study period and especially during post anthesis period (Fig 5.6b). The risk probabilities at

Montcalm were higher during anthesis but later in August, risk probabilities were lower (Fig.

5.6c). Statistically, the risk probabilities exceeding the temperature threshold were significantly higher than climatology for Huron (p = 0.00492) and Montcalm (p = 0.00394) at 99 % confidence level respectively, and for Saginaw (p = 0.04916) at 95 % confidence level (Table

5.3).

The risk probabilities exceeding the RH threshold at all locations followed the climatological trend although it was found to be slightly higher during anthesis (Fig. 5.6d - 5.6f)

Huron had high climatological and forecast risks probabilities exceeding the RH threshold due to vicinity of the Great Lakes (Fig. 5.7e). At Montcalm, post anthesis risk probabilities exceeding

RH threshold were found to be sustained through the milking stage (Fig 5.6f). The risk probabilities exceeding the RH threshold were significantly different from climatology across all locations, with 99 % confidence level.

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Furthermore, the risks probabilities exceeding the rainfall threshold were found to be significantly higher than the climatology during anthesis for all locations (Fig. 5.6g- 5.6i). The risk probabilities exceeding the rainfall threshold in 2018 forecast were found to be highest at

Saginaw, especially during anthesis (Fig. 5.6g). In case of rainfall, the risk probabilities exceeding the threshold were found to be no significant difference at Huron (p = 0.33516) compared to climatology (Table 5.3). However, it was found to be significantly higher than climatology for Saginaw (p = 0.00395) and Montcalm (p = 0.00025) at 99% confidence level.

In case of combined risks probabilities exceeding the T and RH thresholds, the forecast risks probabilities were higher at all locations (Fig. 5.7a – 5.7c). Specifically, the risk was high in the 3rd week of July and lower through 1st week of August followed by the gradual increase in 2nd and 3rd week of August. Huron and Montcalm had sustained risk probabilities during anthesis and post anthesis period (Fig. 5.7b, c). The combined risk probabilities exceeding the threshold of T and RH were found to be significantly different from climatology across all locations at 99

% confidence level.

However, the combined risk probabilities exceeding the threshold of T and R were non- consistent during the period at Saginaw and Montcalm (Fig. 5.7d- 5.7f) and showed a non- significant difference than climatological risk (Table 5.3). It suggests that the combination of rainfall and temperature are not a good predictor of ear rot risks.

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Figure 5. 6. Risk probabilities for temperature (a-c), relative humidity (RH) (d-f) and rain (g-i) at Saginaw (a,d,g), Huron (b,e,h), and Montcalm (c,f,i) for 2018 forecast and climatology (1981-2010). The red arrow indicates predicted mean anthesis days

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For the four extreme scenarios (warm and humid, warm and dry, cool and wet, and cool and dry), the case of warm and humid scenario in Huron and Montcalm showed the greater difference in risk probabilities between forecast and climatology than Saginaw (for temperature risk) (Fig. 5.8a). The risk probabilities exceeding the temperature threshold were highest in case of warm and dry scenario at all locations (Fig. 5.8b). Warm forecasts had higher risk probabilities exceeding the temperature thresholds, than cool forecasts across all locations, for all variables (Fig. 5.8a - 5.8d), which suggests that warm conditions are more favorable for disease development then cool conditions, corroborating Kaatz (2018) and Chilvers (2019).

However, some studies reported cool conditions as more favorable for ear rot disease development (Blaine and Singh, 2018; Wise et al., 2016; Durst and Singh, 2019). For all the studied forecast extreme scenarios, the risk probabilities exceeding the temperature were significantly higher (warm scenarios) and lower (cool scenarios) than climatology at 99% confidence level, except for cool and dry forecast at Montcalm, where it was non-significant

(Table 5.3).

The risk probabilities exceeding the RH thresholds were found to be the highest for the extreme forecast scenario of warm and humid (Fig. 5.9a). Warm and dry forecast showed significantly lower risk than climatology for the risk probabilities exceeding the RH threshold

(Fig. 5.9b). The risks were higher than climatology in humid and wet conditions (Fig. 5.9a,c) as compared to dry conditions (Fig. 5.9b,d) during the season (July-August). It corroborates the findings in several studies (Blaine and Singh, 2018; Wise et al., 2016; Durst and Singh, 2019,

Chilvers, 2019) and contradicts the finding by Kaatz (2018). It suggests that humid or wet conditions are favorable for the disease development. For all the forecast scenarios, the risk

119 probabilities exceeding the temperature were significantly higher (Wet/humid scenarios) and lower (dry scenarios) than climatology at 99% confidence level (Table 5.3).

The risk probabilities exceeding the rainfall threshold were lower than the climatological risk in dry scenarios (Fig. 5.10b, d) as compared to humid/wet scenarios (Fig. 5.10a, c). For all the forecast scenarios, the risk probabilities exceeding the rainfall were significantly higher

(Wet/humid scenarios) and lower (dry scenarios) than climatology at 99% confidence level

(Table 5.3), except for cool and wet forecasts at Montcalm, which was non-significant (Table

5.3). Based on the individual variable, it is difficult to diagnose the risk of disease as the disease development and progression is influenced by combination of weather variables. In order to analyze the effect of combination of variables, we developed risk probabilities for combined variable of T&RH and T&R.

The risk probabilities exceeding the combination of T & RH thresholds were significantly higher than climatological risks in case of warmer scenarios during anthesis period (Fig. 5.11a, b), and lower in case of cooler scenarios (Fig. 5.11c, d). Statistically, the forecast risks were significantly different from climatological risks at 99% confidence level across the location

(Table 5.3). However, in case of T & R, these were non-significant difference than climatology for all locations except for warm and dry forecast scenario at Saginaw (Table 5.3). Specifically, cooler scenarios (Fig. 5.12c, d) showed lower risk probabilities exceeding the threshold of T & R combined than warmer scenarios (Fig. 5.12a, b). The results of suggests that combined T & RH is better predictor/indictor than combined T & R to predict the risk analysis for disease development.

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Figure 5. 8. Risk probabilities for temperature at Saginaw, Huron and Montcalm for (a) warm and humid, (b) warm and dry, (c) cool and wet, (d) cool and dry forecast scenarios and climatology (1981-2010) for all three locations. The red arrow indicates predicted mean anthesis days.

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Table 5. 3. Statistical significance of forecast risk probabilities compared to climatological risks. Location Weather Variables 2018 - Forecast Warm and Warm and Dry Cool and Wet Cool and Dry for risk analysis Humid Forecast Forecast Forecast Forecast Saginaw Temperature 0.04916* 0.00534** 0.00375** 0.00736** 0.00329** RH 0.00429** 0.00387** 0.00695** 0.00915** 0.00726** Rainfall 0.00395** 0.00036** 0.00067** 0.00062* 0.00473** Temp & RH 0.00384** 0.00628** 0.00478** 0.00052* 0.00154** Temp & Rainfall 0.06891ns 0.05789ns 0.04697* 0.08453ns 0.06439ns Huron Temperature 0.00492** 0.00316** 0.00537** 0.00331** 0.00310** RH 0.00363** 0.00128** 0.00925** 0.00557** 0.00289** Rainfall 0.33516ns 0.00023** 0.00817** 0.00385** 0.00516** Temp & RH 0.00451** 0.00429** 0.00358** 0.03486* 0.00159** Temp & Rainfall 0.06876ns 0.06595ns 0.06697ns 0.08474ns 0.06923ns Montcalm Temperature 0.00394** 0.00458** 0.00607** 0.00639** 0.37025ns RH 0.00416** 0.00457** 0.00495** 0.00377** 0.00439** Rainfall 0.00025** 0.00032** 0.00407** 0.09204ns 0.00028** Temp & RH 0.00034** 0.00482** 0.00658** 0.00565** 0.01948* Temp & Rainfall 0.07129ns 0.10017ns 0.06754ns 0.07519ns 0.06193ns *p value at p < 0.05; **p value at p < 0.01; ns Non-significant; RH is daily average relative humidity and temp is daily average temperature.

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5.3.3 Validation of risk analysis model

The developed risk analysis model was validated using observed weather conditions in

2018 and 2017. The DON concentrations were found to be elevated in 2018 than 2017 (Fig.

5.11; Blaine et al., 2018). The DON concentration > 2 µg g− 1 was considered as a threshold for disease severity in this study. The high DON concentration at Washtenaw in 2017 (Fig. 5.11) could be attributed by insect infestation and is out of the scope of this study (Parker et al., 2017;

Blaine et al., 2018).

Figure 5. 13. DON concertation at each location in 2017 and 2018; Source: Blaine et al., (2018)

The observed weather data (rainfall, temperature and RH) in 2018 were used to validate the developed risk analysis model. We also used 2017 to compare and contrast the weather conditions that elevated the risks in 2018 and low disease incidence in 2017. Similar to risk

127 probabilities, the observed weather variables from July 1 to August 31 were used for validation, as this period coincides with the pre- and post-anthesis phase and disease developement

(Munkvold, 2014; Mansfield et al., 2005). The in-vitro risk analysis to study the effects on environmental conditions on mycotoxin production by Marin et al (1999) suggested that warm and moist conditions supported by rain (Munkvold, 2014) during anthesis help in disease spread.

Our results of risk indicator were in agreement with the findings (Fig. 5.12).

For the risk analysis, 0 (no risk) to 1 (high risk) risk factor was used. In Saginaw, the frequency of risk factor due to RH in 2017 was greater than 2018, however, sustained risk of high RH supplemented by sustained high risk due to rainfall in the first week of August made environmental conditions more favorable for ear rot incidence in 2018 (Fig. 5.12a, 5.12c) (e.g.,

Mansfield et al., 2005). The high temperature risk and the combination of T and RH as well as T and R showed high risk during the same periods (Fig. 5.12d, 5.12e). The early grain filling period in the 2nd and 3rd week of August received high rainfall that might had exacerbated the disease severity and hence DON concentration (Munkvold, 2014). During anthesis, Huron and

Montcalm had experienced sustained higher risks due to RH and rainfall in 2018 as compared to

2017 (Fig. 5.12a, 5.12c). However, the combination of T and RH, as well as T and R, during last week of August may have influenced DON concentrations (Vieger et al., 2001). Across all locations, the risks due to combined rainfall and temperature were high during early grain filling period in 2018 and no risk at all in 2017 in case of combined risk of T and RH and T and R. (Fig.

5.12d, 5.12e) (e.g., Vigier et al., 2001; Munkvold, 2014). Rainfall occurence during anthesis followed by dry periods helps in accumulation of pathogens producing mycotoxins (Miller et al.,

2007). Moreover, late rainfall after milking (late August to early September) might be a plausible

128 reason for high mycotoxin concentrations in 2018 at all locations (Fig. 5.2) (Munkvold, 2014;

Battilani and Logrieco, 2014)

In the last two decades, there have been some development in prototype models for predicting DON in maize. However, none of them could validate the threshold and duration of variable’s influence. A simple model using an hourly weather variables from silking to harvesting were used to predict ear rot in maize but they can not be validated (Battilani et al.,

2003; Maiorano et al., 2009). Schaafsma and Hooker (2007) developed DONcast model for wheat and maize to predict ear rot and correlated temperature and rainfall in the period of 10 days prior and 14 days post silking, and found that rainfall > 2mm is favorable for disease development. However, there is a general agreement that it is difficult to predict the disease spread based on the use of only weather variables, as ears are exposed to insect damage too

(Parker et al., 2017).

It is difficult to predict the most accurate combinations of variable and their thresholds, which are favorable to the disease development. The developed risk analysis model can inform the growers to optimize their management strategies during critical crop growth stages using forecast information at a lead-time of 3 months and more. The associated management decisions can be optimized through prediction of anthesis using the calibrated crop growth model

(CERES-Maize) and seasonal climate forecast. The management strategies include, the selection of hybrids, crop rotation (Mabuza et al., 2018), tillage practice (Craven and Nel., 2017), planting date and density (Blandino et al., 2008) can be informed using forecast and risk analysis model for risk aversion of ear rot disease. In-season tactical decision include irrigation (Gxasheka et al.,

2015), and insect control can also inform using advance climate information and risk analysis model (Munkvold, 2014; Owour et al., 2015).

129

(a) (b) (c) (d) (e)

130

5.4 Summary and Conclusions

Climate-sensitive decisions are bound to the horizon of available climate information and hence the usefulness varies with growers and their decisions. The management decisions in maize depend on crop growth stages, based on heat accumulation during the growing season.

Prior information on rainfall and temperature in a season helps in predicting growth stages. In this study, we used the calibrated crop model, CERES-Maize to simulate anthesis using downscaled seasonal climate predictions to evaluate ear rot disease management in corn. The tercile-based seasonal climate forecasts (rainfall and temperature) from NOAA were downscaled at the daily time scale to feed the crop model. An innovative downscaling tool, FResamplerPT, was used in this study, which downscales rainfall and temperature simultaneously. Using the predicted phenology, thresholds of weather variables (rainfall, temperature and humidity), and observed data on disease occurrence, a risk analysis model was developed for ear rot management in corn. Risk analysis model was validated for 2018 observed data and we found that risk of disease were more in 2018 due to combined effect of temperature and RH during and post anthesis period. The rainfall during late milking period also had influence on the disease development. The results from the extreme forecasts suggested that warm forecast scenarios had higher risk than cool forecast scenarios across the location for all the variables. Based on the results of this study we can draw the following conclusions:

• The calibrated crop model (CERES-Maize) can predict phenology of maize using rainfall

and temperature data from the forecast using a new downscaling tool, FResamplerPT.

• The FresamplerPT could downscale the realization of the extreme and normal probabilities

of rainfall and temperature data.

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• The risk analysis model was developed based on threshold of weather variables

(Temperature ≥ 24ᵒ C; Rainfall ≥ 1 mm; Relative Humidity ≥ 80%) during July and

August, which coincides with the disease onset during anthesis.

• For all the studied forecast extreme scenarios, the risk probabilities exceeding the

temperature were significantly higher (warm scenarios) and lower (cool scenarios) than

climatology at 99% confidence level, except for cool and dry forecast at Montcalm, where

it was non-significant.

• The temperature and relative humidity are the best indicators for risk analysis.

5.5 Acknowledgement

Authors would like to thank Corn Marketing Program of Michigan (CMPM) and MSU

AgBioResearch for funding this work. We acknowledge Chubu University, ListenField, USAID-

Philippines and NASA-SERVIR for partly funding Prakash Jha’s research work at MSU.

132

6. CONCLUSIONS

Throughout this dissertation, three main topics were explored. The first topic explored different methods to estimate genetic coefficients of CERES-Maize and evaluate their performance simulating phenology and yield. The second topic explored the multiple model ensemble of ET and derivation of crop coefficients for maize irrigation scheduling. The third topic explored the development of risk analysis model using predicted phenology of the above calibrated crop model and seasonal climate forecast. The first and third topics were examined at different locations from the Michigan Maize Performance Trial program, and the second topic was examined at two fields under continuous maize rotation with eddy covariance flux towers, situated in Kalamazoo, Michigan. The resulting region-specific kc-curves were evaluated and tested at a farmer’s field in southwest Michigan.

In the first study, the new method, Noisy Monte Carlo Genetic Algorithm (NMCGA) was introduced for estimating genetic coefficients, and compared with existing methods, Genotype

Coefficient Calculator (GENCALC) and Generalized Likelihood Uncertainty

Estimation (GLUE). The coefficients from the ensemble of multiple methods and individual methods were evaluated for prediction of phenology and yield. From this study, the major conclusions drawn are as follows:

• The multi-model ensemble of genetic coefficients minimized the biases associated with

individual methods.

• The ensembled genetic coefficients improved predictions of phenology and yields.

• Under water stressed conditions, soil root growth factor needs to be adjusted to allow

hybrid maize to explore available water and nutrients in the soil profile.

133

In the second study, the multi-model ensemble of ET estimates (RS-based SEBAL,

Priestley-Taylor-JPL, MODIS Penman-Monteith and a ground-based empirical model, FAO-kc) were used to derive crop coefficients at two locations in Michigan for water management. This study also evaluated, if standard FAO-Kc values are generally applicable in all situations. The derived crop coefficients were compared with standard FAO-Kc approach for irrigation scheduling. From this study, the major conclusions drawn are as follows:

• The calibrated crop model simulated crop development stages can be used to map crop

coefficients (Kc) to reflect the genetic characteristics of the maize hybrid.

• The multi-model ensemble of ET estimates reduced the uncertainty and biases associated

with individual methods, hence, gave better information in the development of crop

coefficients (Kc).

• The standard FAO based Kc curve for maize cannot be generally applicable in all

situations hence, it must be derived for specific locations.

• The derived crop coefficients can improve irrigation scheduling, minimizing losses due to

over irrigation.

In the third study, the calibrated model was used to predict anthesis using seasonal climate forecast. The innovative downscaling tool, FResamplePT, which downscales probabilities of rainfall and temperature from seasonal climate forecast was used in this study.

Based on the forecast information, risk probabilities exceeding the thresholds of weather variables (temperature, rainfall, relative humidity and combination of these) were developed, which indicates favorable conditions to ear rot infection in maize. The risk analysis model developed on this basis was validated in 2018. From this study, the major conclusions drawn are as follows:

134

• The calibrated crop model can predict phenology using rainfall and temperature forecasts

downscaled by FResamplerPT.

• Forecast-based ear rot risk probabilities can help growers’ preparedness for managing ear

rot in maize.

• Temperature and relative humidity are the best indictors for ear rot disease risk

assessments.

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7. FUTURE RESEARCH RECOMMENDATIONS

Possible avenues for future research are as follows:

• Field experiments to study water stress at different soil depths should be performed to

validate the veracity of our hypothesis on the adjustment of root growth factor under

water-stressed conditions.

• The NMCGA can be integrated in DSSAT to provide users more multi-method

comparison and ways for ensembling genetic coefficients.

• Open-source ET ensemble framework for irrigation planning can be developed.

• In depth study understanding why SEBAL overestimated ET and PT-JPL underestimated

ET.

• Using ECOSTRESS to estimate site-specific kc-curves.

• Linking sub-seasonal to seasonal (S2S) with the ear rot risk assessment model.

• Develop a software tool that integrates the crop model, climate forecast and ear rot

disease assessment model for decision support to growers.

136

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