TESTING AND IMPROVEMENT OF THE DSSAT CROP GROWTH MODELS FOR CROPPING SYSTEMS ANALYSIS

• Sponsoring Institution: National Institute of Food and Agriculture
• Project Status: COMPLETE
• Funding Source: HATCH
• Reporting Frequency: Annual
• Accession No.: 1009843
• Project Start Date: May 25, 2016
• Project End Date: May 15, 2021
• Program Code: [(N/A)]- (N/A)

Recipient Organization
AUBURN UNIVERSITY
108 M. WHITE SMITH HALL
AUBURN,AL 36849

Performing Department
Biosystems Engineering

Non Technical Summary
It is projected that by the year 2050, the world's population will increase from 7 to over 9 billion people. The size of the global middle class population is expected triple from 1 to 3 billion people as well. With the projected increase in population, especially the number of global middle class citizens, it is expected that the world will need to increase food production by 70-100% from current levels by 2050. During this same period, greenhouse gas emissions will lead to changes in rainfall and temperature patterns in Alabama and around the world, which will impact global water, carbon and nutrient cycles. Intensification of agricultural production since the first green revolution has led to increased use of nitrogen and irrigation to increase crop yields. For instance, more intense nitrogen use in the Midwestern US has resulted in N losses through the tile drainage systems and into the streams and rivers, and is a major factor in the creation of a dead zone in the Gulf of Mexico. More intense N use in the North China Plain (NCP) has led to increased N levels in the wells and underground aquifers, which supply water to millions of people. Increased use of irrigation has led to rapidly dropping aquifers around the world, such as the Ogallala aquifer in the US and the North China Plain aquifer, which is dropping at the rate of 1-m per year. Researchers at NASA recently reported that 21 of the largest 37 aquifers around the world are being depleted. Nitrogen and water are major factors in increasing global food production, but N is producing negative environmental consequences and water is becoming limited around the world.Developing adaptive management strategies is critical to mitigating the effects of climate change and climate variability on local, regional and world crop production. Researchers across the world are searching for techniques to test adaptive management strategies including changes in water, nitrogen, and soil management, as well as crop species, genetics and rotations, or switching from crops to livestock. There are many ongoing field trials to study adaptive management strategies. For instance, Free Air Carbon Exchange (FACE) experiments have been constructed in fields to evaluate the impact of elevated CO2 on growth of important agronomic crops. While these trials are important to quantify and understand the underlying behavior of various management strategies, it is difficult to extrapolate these field specific results to the regional or global scales because they do not represent different temperature and rainfall patterns that may exist under future climate change scenariosCrop models are often used by researchers to extrapolate the results of point specific field trials across a larger area, and to test the expected performance of different management strategies at the large scale. The objectives of this work are to validate the nitrogen and water response of several of the DSSAT crop growth models in Alabama, make model improvements where necessary, and to use the models to evaluate adaptive management strategies for climate change, and evaluate trade-offs between water use and environmental impact of nitrogen and producer profits.

Animal Health Component

75%

Research Effort Categories

Basic

(N/A)

Applied

75%

Developmental

25%

Classification

Knowledge Area (KA)	Subject of Investigation (SOI)	Field of Science (FOS)	Percent
205	0199	1020	50%
102	0199	1020	50%

Knowledge Area
102 - Soil, Plant, Water, Nutrient Relationships; 205 - Plant Management Systems;

Subject Of Investigation
0199 - Soil and land, general;

Field Of Science
1020 - Physiology;

Keywords

crop model

dssat

climate change

water use

nitrogen management

Goals / Objectives
The overall goal of this project is to test and improve several of the DSSAT crop growth models to improve their ability to evaluate cropping system performance, environmental impact of N management, and response to climate change. The specific objectives are:1. Develop datasets and perform DSSAT model calibration and validation and use the validated models to study optimization of water and managment in important cropping systems in Alabama and around the world2) Develop methods to simulate cropping systems responses across spatial scales3) Develop improvements to various DSSAT models to simulate the effects of cropping systems, management, and environment on production

Project Methods
Objective 1 - Develop datasets and perform DSSAT model calibration and validation and use the validated models to study optimization of water and management in important cropping systems in Alabama and around the worldField Level Calibration and Validation in Alabama - We will work with researchers at several Universitieswho have collected plant growth, soil and management data for historic experiments. These data will be used for model calibration, validation, and to explore field level comparisons of alternative crop management and rotation strategies.Corn-Wheat Systems - A 4-yeardataset consisting of winter wheat-summer maize collected in Quzhou, China will be used for model validation. Treatments consisted of 5 or 6 N rates and standard producer production practices were followed. Detailed plant growth and develop data, including N concentration and soil N and water content were collected. Soil properties and daily weather data are readily available for crop model calibration and validation.These datasets will be divided into calibration and validation years. The DSSAT maize and wheat models will be tested to determine how they represent crop response to N levels by comparing simulated and observed plant characteristics. The models will then be used to study the best long-term N management practices over multiple seasons using at least 30 years of historical weather data. We will examine the impact of N timing and rate on crop growth and yield, as environmental impact represented by N left in the soil at the end of the season or lost by leaching from the system. Probability curves will be developed to determine the linkage between N management strategy, yield, and environmental impact of production systems.Wheat response to CO2 and N -Tests of the CERES-Wheat model to environment, elevated CO2 and N rates will be conducted using data collected by the Institute of Environment and Sustainable Development at the Chinese Academy of Agricultural Sciences. The Institute has collected over 20 years of wheat growth and yield data at approximately 200 locations in the North China Plain (NCP). These experiments represent numerous cultivars and some locations and years have different N application rates. A subset of this data where adequate measurements were made for model calibration and validation will be identified to test the CERES-Wheat model for the North China Plain. Soil data required to run the model is available from the Chinese soil survey (Shangguan et al., 2013) and weather data is available from a weather station network across the NCP. Methods from the literature designed to calculate genetic coefficients across multiple locations and years will be usedto compute genetic coefficients for themodel. The datasets will be divided into model calibration sites and model validation sites. Calibration of genetic coefficients will be performed on the high N sites across the calibration locations. The model will then be tested on the validation dataset and performance evaluated using the root mean square of error between simulated and observed growth and development data.The Institute has also collected 3-years of wheat growth data under field conditions using a Free-air CO2 enrichment system at an experimental site in Changping near Beijing. The treatments include two CO2 levels (415 ppm and 550 ppm) and two nitrogen levels (170 kg N ha-1 and 100 kg N ha-1) and two varieties. They have collected sufficient data to test the CERES-Wheat model response to CO2 and N. They have observed different photosynthetic responses to CO2 for the two varieties, both overall and during different growth stages. We will use this information to develop new inputs for the CERES-Wheat model that will allow different cultivars to have different photosynthetic responses to CO2. The overall dataset will be used to improve the response of CERES-Wheat to both CO2 and N under elevated CO2.Rice N Management- This work will be primarily based upon an experiment that was conducted by China Agricultural University at a sitelocated in the Sanjiang Plain in the Heilongjiang Province. Five N rates (0, 70, 100, 130 and 160 kg N/ha) were applied to a single cultivar in 2012, 2013, 2014 and 2015. Rice management followed best management practices in the region, except irrigation, which followed a new wet and dry irrigation strategy. Measurements of plant part biomass and plant N content weremade 4-5 times during the season, as well as date of development growth stages. Irrigation application rates were measured, and there is adequate soil information required for model inputs. The model will be calibrated and validated for this site in the heart of the North China Plain, and used to assess adaptive management strategies under different climate change scenarios. Objective 2: Develop methods to simulate cropping systems responses across spatial scales, including AlabamaRegional Yield Calibration - To evaluate regional impacts of climate variability and climate change, it is standard practice to calibrate and validate crop models at a number of point locations in the region that are representative of different soil types in the region. The calibrated model can then be used to evaluate crop response under different climate change scenarios and to evaluate adaptive strategie.Techniques developed by my previous research group will be used to calibrate soil properties at the regional scale. We will apply these techniques at point locations in Alabama and perhaps other locations across the southeastern US using yield trial data collected by Agricultural Experiment Stations on an annual basis.Objective 3 - Develop improvements to various DSSAT models to simulate the effects of cropping systems, management, and environment on productionThe current sugar beet model is in the preliminary stages of development. Anar et al. (2015) modified the existing CERES-Maize model to simulate sugar beet growth. Many of the growth and development functions in CERES-Maize do not translate well to sugarbeet physiology. We plan to improve the existing sugar beet model to determine best management practices for precision management of sugarbeets. Three years of field experiments will be conducted near Stuttgart, Germany to collect growth and development data for model development. Additional data available from KWS will also be used to develop genetic coefficients for different commercial varieties. Experiments will be designed to measure biomass accumulation and developmental growth stages required to develop new model theory to better simulate sugar beet growth.

Progress 05/25/16 to 05/15/21

Outputs
Target Audience:US and international farmers and governments concerned with policymaking. Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided?None due to Covid travel restrictions. How have the results been disseminated to communities of interest?Peer reviewed articles have been submitted for consideration of publication. What do you plan to do during the next reporting period to accomplish the goals?This is the final report for this project.

Impacts
What was accomplished under these goals? 1. Methods were developed to simulate the performance of drought tolerant peanuts across the southeastern US. 2. Methods were developed and incorporated into the DSSAT-CROPGRO-Peanut model to simulate drought tolerant peanuts. 3. Methods were developed to use molecular markers to compute inputs to the CERES-Maize model.

Publications

• Type: Journal Articles Status: Published Year Published: 2021 Citation: 1. Liu, Y., B. Cui, W.D. Batchelor and C. Zhang. 2021. Evaluation on the meteorological service for mitigating the severe impacts of typhoon Rammasun. Tropical Conversation Science 14:1-9. DOI: 10.1177/1940082921992660.
• Type: Journal Articles Status: Published Year Published: 2021 Citation: 2. Liu, Z., H. Ying, M. Chen, J. Bai, Y. Xue, Y. Yin, W.D. Batchelor, M. Du, Y. guo, Q. Zhang, Z. Cui, F. Zhang and Z. Dou. 2021. Optimization of Chinas maize and soy production can ensure feed sufficiency at lower nitrogen and carbon footprints. Nature Food 2021 (2):426-433. https://doi.org/10.1038/s43016-021-00300-1.
• Type: Journal Articles Status: Published Year Published: 2021 Citation: 4. Wang, X., Y. Miao, W.D. Batchelor, R. Dong and K. Kusnierek. 2021. Evaluating model-based strategies for in-season nitrogen management of maize using weather data fusion. 2021. Agricultural and Forest Meteorology 308-309 (2021) 108564. https://doi.org/10.1016/j.agrformet.2021.108564
• Type: Journal Articles Status: Published Year Published: 2021 Citation: 5. Liu, Z., X. Yang, R. Xie, X. Lin, T. Li, W.D. Batchelor, J. Zhao, Z. Zhang, S. Sun, F. Zhang, Q. Huang, Z. Su, K. Wang, B. Ming, P. Hou and S. Li. 2021. Prolongation of the grain filling period and change in radiation simultaneously increased maize yields in China. Agricultural and Forest Meteorology 308-309 (2021) 108573. https://doi.org/10.1016/j.agrformet.2021.108573.
• Type: Journal Articles Status: Published Year Published: 2021 Citation: 3. Gai, H., T. Yan, A. Zhang, W.D. Batchelor and Y. Tian. 2021. Exploring factors influencing farmers continuance intention to crop residue retention: Evidence from rural China. Environmental Research and Public Health 2021, 18, 7412. https://www.mdpi.com/1660-4601/18/14/7412

Progress 10/01/19 to 09/30/20

Outputs
Target Audience:The target audience for this project is scientists who use the Decision Support System for Agrotechnology Transfer (DSSAT) cropping systems model. Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided?The PI provided training to 3 graduate students inGermany and 7 students and researchers in China. The focus of the training was primarily DSSAT model calibration and evaluation. Most of the training took place as distance training due to travel restrictions. How have the results been disseminated to communities of interest? Nothing Reported What do you plan to do during the next reporting period to accomplish the goals?1) Develop methods withcollaborators in China to use molecular markersto estimate genetic inputs to the CERES-Maize model. 2) Incorporate drought tolerance responses into the CROPGRO-Peanut model. 3) Continue to improve methods to simulate historic county level yield for peanuts. 4) Continue testing the DSSAT models (wheat, maize, rice, sugarbeet) under China conditions to evaluate best management practices.

Impacts
What was accomplished under these goals? 1) Methods were developed to simulate county level peanut yields in the US by adjusting soil andgenetic properties to minimize error between simulated and observed yields over 8-10 seasons. The method was tested in several states (AL, GA, FL, TX) and the R2for simulated vs observed county level yields ranged from 0.32 - 0.85. 2) The DSSAT sugarbeet model was tested in Germany to determine if it could accurately simulate leaf disease damage. Results were excellent and an article was published on the work. A project was initiated in Central China to evaluate the sugarbeet model. Preliminary results indicate the model does a good job simulating yield under different populations and nitrogen rates, but tends to under predict leaf number, leaf area index and canopy weight. 3) A 2nd year of data was collected to evaluate peanut drought response mechanisms for new peanut varieties. Data will be used to incorporate drought tolerance response in the CROPGRO-Peanut model. 4) Pest damage routines were incorporated into theCERES-Maize model. We used the model to evaluate the impact of maize leaf necrosis on maize growth and yield in Kenya. The model performed well, and a journal article resulted from the work.

Publications

• Type: Journal Articles Status: Published Year Published: 2020 Citation: Memic, E., S. Graeff-Honninger, O. Hensel and W.D. Batchelor. 2020. Extending the CSM-CERES-Beet model to simulate impact of observed leaf disease damage on sugar beet yield. Agronomy 2020, 10, 1930; doi:10.3390/agronomy10121930.
• Type: Journal Articles Status: Published Year Published: 2020 Citation: Cammarano, D., H. Zha, L. Wilson, Y. Li, W.D. Batchelor and Y. Miao. 2020. A remote sensing-based approach to management zone delineation in small scale farming systems. Agronomy 2020, 10, 1767; doi:10.3390/agronomy10111767.
• Type: Journal Articles Status: Published Year Published: 2020 Citation: Li, D., W.D. Batchelor, D. Zhang, H. Miao, H. Li, S. Song and R. Li. 2020. Analysis of melatonin regulation of germination and antioxidant metabolism in different wheat cultivars under polyethylene glycol stress. PLoS ONE15(8):e0237536. https://doi.org/10.1371/journal.pone.0237536
• Type: Journal Articles Status: Published Year Published: 2020 Citation: Liu, T. X., Yang, W.D. Batchelor, Z. Liu, Z. Zhang, N. Wan, S. Sun, B. He, J. Gao, F. Bai, F. Zhang and J. Zhao. 2020. A Case Study of climate-smart management of foxtail millet (Setaria italica) under future climate change in Lishu county of Jilin, China. Agricultural and Forest Meteorology 292-293 (2020) 108131.
• Type: Journal Articles Status: Published Year Published: 2020 Citation: Hu, K., H. Liang, H. Lv, W.D. Batchelor, X. Lian, Z. Wang, and S. Lin. 2020. Simulating DON leaching and optimizing water and N management practices for greenhouse vegetable production systems. Agricultural Water Management 241 (2020) 106377.
• Type: Journal Articles Status: Published Year Published: 2020 Citation: Batchelor, W.D., L.M. Suresh, X. Zhen, Y. Beyene, M. Wilson, G. Kruseman and B. Prasanna. 2020. Simulation of maize lethal necrosis (MLN) damage using the CERES-Maize model. Agronomy 2020, 10, 710; doi:10.3390/agronomy10050710.
• Type: Journal Articles Status: Published Year Published: 2020 Citation: Shi, Xinrui, W.D. Batchelor, H. Liang, S. Li, B. Li and K. Hu. 2020. Determining optimal water and nitrogen management under different initial residual soil mineral nitrogen levels in northwest China based on a model approach. Agricultural Water Management 234(2020): 106110.
• Type: Journal Articles Status: Published Year Published: 2020 Citation: Yang, W., H. Feng, X. Zhang, J. Zhang, J. Doonan, U. Schurr, W.D. Batchelor, L. Xiong and J. Yang. 2020. Crop phenomics and high-throughput phenotyping: past decades, future and challenges. Molecular Plant 13, 187-214, February 2020.
• Type: Journal Articles Status: Published Year Published: 2020 Citation: Shi, Xinrui, K. Hu, W.D. Batchelor, H. Liang, Y. Wu, Q. Wang, J. Fu, X. Cui and F. Zhou. 2020. Exploring optimal nitrogen management strategies to mitigate nitrogen losses from paddy soil in the middle reaches of the Yangtze River. Agricultural Water Management 228(2020):105877.

Progress 10/01/18 to 09/30/19

Outputs
Target Audience:The target audience for this project is scientists who use the Decision Support System for Agrotechnology Transfer (DSSAT) cropping systems model. Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided?The PI served as an instructor at the annual International DSSAT Training Workshop, May 19-25, 2019. The PI has also provided training to students at China Agricultural University to use the DSSAT models to evaluate farming systems in China. How have the results been disseminated to communities of interest? Nothing Reported What do you plan to do during the next reporting period to accomplish the goals?1. Complete evaluation of pest damage routines in the Maize to simulate maize lethal necrosis disease in maize in Africa. 2. Extend testing of methods to calibrate the DSSAT models at the county scale in the US and China. 3. Continue testing the DSSAT models (wheat, maize and rice) under China conditions to evaluate best management practices.

Impacts
What was accomplished under these goals? 1) Methods were developed to simulate county level maize yields by adjusting soil and management properties to minimize error between simulated and observed yields over multiple seasons. This method was tested for a county in China, and gave R2 values of 0.9 over 10 seasons. 2) Code developed in previous years to simulate pest damage in the CSM-CERES-Wheat model was finalized and submitted to the DSSAT development team for consideration of distribution in future releases of DSSAT. 3) The DSSAT wheat and maize models were tested at several locations in China and used to evaluate long-term yield gaps and optimum water and nitrogen management strategies. 4) The sugarbeet model was evaluated for conditions in Germany.

Publications

• Type: Journal Articles Status: Published Year Published: 2019 Citation: Liang, H., K. Hu, W.D. Batchelor, Q. Chen, B. Liang and B. Li. 2019. Modeling dissolved organic nitrogen leaching under different N management practices for the intensive greenhouse production using the improved WHCNS_veg model. Geoderma 337(2019):1039-1050.
• Type: Journal Articles Status: Published Year Published: 2019 Citation: Anar, M.J., Z. Lin, G. Hoogenboom, V. Sheila, W.D. Batchelor, J. Teboh, M. Ostlie, B. Schatz and M. Khan. 2019. Modeling growth, development and yield of sugarbeet using DSSAT. Agricultural Systems 169:58-70.
• Type: Journal Articles Status: Published Year Published: 2019 Citation: Roll, G., W.D. Batchelor, A. Castro, M. Simon, and S. Graeff. 2019. Development and evaluation of a leaf disease damage extension in CSM-CERES Wheat. Agronomy 9(3):120; https://doi.org/10.3390/agronomy9030120
• Type: Journal Articles Status: Published Year Published: 2019 Citation: Zhang, D., D. Li, H. Wang, H. Li, H. Ju, R. Li, W.D. Batchelor and Y. Li. 2019. Identifying plant density and nitrogen best management practices for winter wheat production under limiting irrigation conditions in North China Plain using DSSAT-CERES-Wheat model. Nutrient Cycling in Agroecosystems 114(1):19-32.
• Type: Journal Articles Status: Published Year Published: 2019 Citation: Memic, E., S. Graeff, W.D. Batchelor. 2019. Extending the CERES-Beet model to simulate leaf disease in sugar beet. In: 12th European Conference on Precision Agriculture (edited by: John V. Stafford), pp 977-983. Montpellier, France (DOI: 10.3920/978-90-8686-888-9_120).
• Type: Journal Articles Status: Published Year Published: 2019 Citation: Zha, H., D. Cammarano, L. Wilson, Y. Li, W.D. Batchelor and Y. Miao. 2019. Combining crop modelling and remote sensing to create yield maps for management zone delineation in small scale farming systems. In Stafford, J.V. (Ed), Precision Agriculture 2019, Wageningen Academic Publishers, The Netherlands. pp 883-890 DOI: 10.3920/978-90-8686-888-9
• Type: Journal Articles Status: Published Year Published: 2019 Citation: Liu, Z., Y. Yin, J. Pan, Y. Hao, D. Lu, W.D. Batchelor, W. Ma, Z. Cui. 2019. Yield gap analysis of county level irrigated wheat production in Hebei Province, China. Agronomy 111(5):2245-2254.

Progress 10/01/17 to 09/30/18

Outputs
Target Audience:The target audience for this project is other users of the Decision Support System for Agrotechnology Transfer (DSSAT) cropping systems model. The model has over 6000 subscribers around the world who use the model to evaluate optimum cropping systems, evaluate impacts of climate change on cropping systems, design resilient cropping systems, evaluate impact of genetics across the landscape, and evaluate precision management systems. Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided?The PI served as an instructor at the annual International DSSAT Training workshop, May 14-19, 2018. The PI also provided training on the DSSAT model to 35 extension workers in Senegal March 2-16, 2018 under the Farmer to Farmer program sponsored by Winrock. How have the results been disseminated to communities of interest? Nothing Reported What do you plan to do during the next reporting period to accomplish the goals?During the next reporting period, we plan the following activities: - continue development of the sugarbeet model and initiate a validation with data collected in 2017-2018 in Germany, as well as other historical datasets. - Conduct a more extenstive evaluation of the CERES-Rice model for the cool conditions in the North China Plain. - Develop a proof of concept of using the CERES-Maize model to simulate disease damage in Africa. - Initiate work on evaluating the CERES-Wheat model for developing resilient cropping systems in the North China Plain.

Impacts
What was accomplished under these goals? Simulating Pest Damage in Wheat - The DSSAT v4.7 crop modeling family of models is widely used around the world to simulate management and production of many different crops. The CSM-CERES-Wheat model is distributed with the DSSAT software and simulates wheat production. This year, we integrated pest damage into the CSM-CERES-Wheat model. Pest damage included damage to leaf, stem, grain, and root mass, leaf area index and assimilate reduction. The user can enter time series measurements of pest damage into the time series model input file. The model reads the daily damage and applies the damage to the appropriate state or rate variable. The code has been verified and tested using data for disease damage collected in Argentina. It is anticipated that the model code will be released in 2019. Simulating Disease Damage in Maize - Diseases such as maize leaf necrosis (MLN), maize streak virus (MSV), grey leaf spot (GLS) and turcicum blight (ET) are a major source of yield loss in Sub-Saharan Africa. Breeders can benefit from tools to help understand the impact of these diseases on maize yields. The CERES-Maize model distributed with DSSATv4.6 has the capability to simulate the impact of foliar diseases on maize growth and yield. The purpose of these projects was to develop and test a method to simulate the impacts of diseases on maize growth and yield. Disease progress curves for MNL were translated into daily damage curves for defoliation and dead leaf area and chlorosis. A field experiment consisting of 17 maize hybrids ranging in level of MLN tolerance were planted under inoculated and non-inoculated conditions. Model based genetic coefficients for each maize hybrid were derived using data collected for a 2nd year in the non-inoculated treatment and evaluated using data from the inoculated treatment. Overall, the model performed well in simulating the impact of MLN damage on maize yield. The model gave an R2 of 0.98 for simulated vs observed yield for the calibration (non-inoculated) dataset and an R2 of 0.92 for the evaluation (inoculated treatment) dataset. Techniques developed in this project can likely be transferred to other maize disease types. The key to simulating other diseases is to develop the appropriate relationship between disease severity rating and cumulative leaf defoliation and dead leaf area and percent leaf chlorosis.

Publications

• Type: Journal Articles Status: Published Year Published: 2018 Citation: Liang, H., Hu, K., W.D. Batchelor, W. Qin and B. Li. 2018. Developing a water and nitrogen management model for greenhouse vegetable production in China: sensitivity analysis and evaluation. Ecological Modeling 36:24-33. Zhang, D., R. Li, W.D. Batchelor, J. Hui and Y. Li. 2018. Evaluation of limited irrigation strategies to improve water use efficiency and wheat yields in the North China Plain. PLOS ONE 13(1):e0189989. Memic, E., S. Graeff, W. Claupein and W.D. Batchelor. 2018. GIS-Based spatial nitrogen management model for maize ⿿ short and long term marginal net return maximizing nitrogen application rates. Precision Agriculture (2018). https://doi.org/10.1007/s11119-018-9603-4. Zhen, X., H. Shao, W. Zhang, W. Huo, W.D. Batchelor, P. Hou, E. Wang, G. Mi, Y. Miao, H. Li and F. Zhang. 2018. Testing a bell-shaped function for estimation of fully expanded leaf area in modern maize under potential production conditions. The Crop Journal (2018). https://doi.org/10.1016/j.cj.2018.03.008. Xu, R., H. Tian, S. Pan, S. Prior, Y. Feng, W.D. Batchelor, J. Chen, and J. Yang. 2018. Global ammonia emissions from synthetic nitrogen fertilizer applications in agricultural systems: empirical and process-based estimates and uncertainty. Global Change Biology https://doi.org/10.1111/gcb.14499. Zhang, J., Y. Miao, W.D. Batchelor, J. Lu, H. Wang and S. Kang. 2018. Estimation of optimum nitrogen rates for cool season rice in Northeastern China using the CERES-Rice model. Agronomy 8:263; doi:10.3390/agronomy8110263.

Progress 10/01/16 to 09/30/17

Outputs
Target Audience:The target audience for this project is other users of the Decision Support System for Agrotechnology Transfer (DSSAT) cropping systems model. The model has over 6000 subscribers around the world who use the model to evaluate optimum cropping systems, evaluate impacts of climate change on cropping systems, design resilient cropping systems, evaluate impact of genetics across the landscape, and evaluate precision management systems. This project provided training to Report Date 01/15/2017 Page 3 of 4 United States Department of Agriculture Progress Report Accession No. 1009843 Project No. ALA014-1-16016 researchers and graduate students at four international institutions during this period. Most of the training was in the form of small group training to teach researchers and students how to set up and calibrate the model. Several lectures were also given, as well as a 6-hour symposium. Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided?The PI served as an instructor at the annual International DSSAT Training workshop, May 15-20, 2017. How have the results been disseminated to communities of interest? Nothing Reported What do you plan to do during the next reporting period to accomplish the goals?During the next reporting period, we plan the following activities: - continue development of the sugarbeet model and initiate a validation with data collected in 2017 in Germany, as well as other historical datasets. - Conduct a more extenstive evaluation of the CERES-Rice model for the cool conditions in the North China Plain. - Develop a proof of concept of using the CERES-Maize model to simulate disease damage in Africa. - Initiate work on evaluating the CERES-Wheat model for developing resilient cropping systems in the North China Plain.

Impacts
What was accomplished under these goals? Simulating Pest Damage in Wheat - The DSSAT v4.7 crop modeling family of models is widely used around the world to simulate management and production of many different crops. The CSM-CERES-Wheat model is distributed with the DSSAT software and simulates wheat production. This year, we integrated pest damage into the CSM-CERES-Wheat model. Pest damage included damage to leaf, stem, grain, and root mass, leaf area index and assimilate reduction. The user can enter time series measurements of pest damage into the time series model input file. The model reads the daily damage and applies the damage to the appropriate state or rate variable. The code has been verified and tested using data for disease damage collected in Argentina. It is anticipated that the model code will be released in 2018. Simulating Disease Damage in Maize - Diseases such as maize leaf necrosis (MLN), maize streak virus (MSV), grey leaf spot (GLS) and turcicum blight (ET) are a major source of yield loss in Sub-Saharan Africa. Breeders can benefit from tools to help understand the impact of these diseases on maize yields. The CERES-Maize model distributed with DSSATv4.6 has the capability to simulate the impact of foliar diseases on maize growth and yield. The purpose of these projects was to develop and test a method to simulate the impacts of diseases on maize growth and yield. Disease progress curves for MNL were translated into daily damage curves for defoliation and dead leaf area and chlorosis. A field experiment consisting of 17 maize hybrids ranging in level of MLN tolerance were planted under inoculated and non-inoculated conditions. Model based genetic coefficients for each maize hybrid were derived using data collected in the non-inoculated treatment and evaluated using data from the inoculated treatment. Overall, the model performed well in simulating the impact of MLN damage on maize yield. The model gave an R2 of 0.98 for simulated vs observed yield for the calibration (non-inoculated) dataset and an R2 of 0.92 for the evaluation (inoculated treatment) dataset. Techniques developed in this project can likely be transferred to other maize disease types. The key to simulating other diseases is to develop the appropriate relationship between disease severity rating and cumulative leaf defoliation and dead leaf area and percent leaf chlorosis.

Publications

• Type: Journal Articles Status: Published Year Published: 2017 Citation: Li, X.X., J. Hui, S. Garre, Y. Chang-rong, L. Qin, W.D. Batchelor and Q. Liu. 2017. Spatiotemporal variation of drought characteristics in the context of climate change in the Huang-Huai-Hai plain. China. Journal of Integrative Agriculture 16(0):60345-7. DOI 10.1016/S2095-3119 (16) 61545-9.
• Type: Journal Articles Status: Published Year Published: 2017 Citation: Zhang*, J., Y. Miao and W.D. Batchelor. 2017. Evaluation of the CERES-Rice model for precision nitrogen management for rice in northeast China. 2017. Advances in Animal Biosciences 8(2): 328-332.
• Type: Journal Articles Status: Published Year Published: 2017 Citation: Memic*, E., S. Graeff, W. Claupein and W.D. Batchelor. 2017. GIS-Based spatial nitrogen management model for maize. Advances in Animal Biosciences 8(2): 312-316.
• Type: Journal Articles Status: Accepted Year Published: 2018 Citation: Liang, H., Hu, K., W.D. Batchelor, W. Qin and B. Li. 2018. Developing a water and nitrogen management model for greenhouse vegetable production in China: sensitivity analysis and evaluation. Ecological Modeling 36:24-33.

Progress 05/25/16 to 09/30/16

Outputs
Target Audience:The target audience for this project is other users of the Decision Support System for Agrotechnology Transfer (DSSAT) cropping systems model. The model has over 6000 subscribers around the world who use the model to evaluate optimum cropping systems, evaluate impacts of climate change on cropping systems, design resilient cropping systems, evaluate impact of genetics across the landscape, and evaluate precision management systems. This project provided training to researchers and graduate students at three international institutions during this period. Most of the training was in the form of small group training to teach researchers and students how to set up and calibrate the model. Several lectures were also given, as well as a 6-hour symposium. ? Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided?During the past year, the PI provided five weeks of training to several scientists and graduate students at the University of Hohenheim, China Agricultural University, and the China Academy of Agricultural Sciences. Each of these institutes are committed to providing data to support this project. Training involved mostly hands-on small group training on how to set up and calibrate the CERES-Wheat, CERES-Maize and CERES-Rice models in the DSSAT cropping systems package. Several lectures and a 6-hour intense training session were given to larger groups. How have the results been disseminated to communities of interest? Nothing Reported What do you plan to do during the next reporting period to accomplish the goals?During the next reporting period, we plan the following activities: - continue development of the sugarbeet model and initiate a validation with data collected in 2017 in Germany, as well as other historical datasets. - Conduct a more extenstive evaluation of the CERES-Rice model for the cool conditions in the North China Plain. - Develop a proof of concept of using the CERES-Maize model to simulate disease damage in Africa. - Initiate work on evaluating the CERES-Wheat model for developing resilient cropping systems in the North China Plain.

Impacts
What was accomplished under these goals? Impact - Rice is a staple food crop for China, which is the world's most populous country. While yields are increasing, nitrogen (N) use is also increasing to the point of contaminating the water supply. Most rice is grown at a small scale (< 1 ha fields), however large scale (20-25 ha) rice farms are located in Jiansanjiang, Heilongjiang Province in Northeast China. In this work, we used the DSSAT-Rice model to analyze five years of rice response to nitrogen (N) data collected (2011-2015) in order calibrate and evaluate the model for cool season rice response to N. The model was then run for 15 seasons of historical weather data to estimate the long term economic optimum N rate for this region. The model indicated that 120-130 kg N ha-1 was the long term economic optimum N rate, while the producer current practice is closer to 200 kg N ha-1. This indicates that producers are leaving on average 70-80 kg N ha-1 for leaching into groundwater supply. 1. Develop datasets and perform DSSAT model calibration and validation and use the validated models to study optimization of water and management in important cropping systems in Alabama and around the world Nitrogen Management in Rice - In partnership with China Agricultural University, we calibrated and evaluated the CERES-Rice model for different N rates in the cool region of the Heilongjiang Province in Northeast China. Historical experimental data collected from 2011-2015 were used for this analysis. The experimental site was located in Jiansanjiang, on an experimental farm managed by China Agricultural University. The soil consisted of wet black clay approximately 20 cm thick. The soil has high organic matter (>4%) and is highly fertile and excellent for agricultural production. Each experiment consisted of five N rate treatments including 0, 70, 100, 130, and 160 kg N ha-1 in 2011 and 2013 and 0, 40, 80, 120, and 160 kg N ha-1 in 2014 and 2015. The variety planted each year was Longjing 21, which is a 12 leaf variety. Experiments were replicated 3 times. N fertilizer was distributed in three applications: 40% as basal N before transplanting, 30% at the tillering stage and 30% at the stem elongation stage. All treatments utilized urea as the N source. All other field management practices including planting and harvest dates and pest management followed the local recommended practices. Weather data including daily maximum and minimum temperature, rainfall, and sunshine hours were collected from the local weather station. Biomass measurements including canopy and grain weight and leaf area index (LAI) were collected in each experiment and year. The years 2014 and 2015 were used to calibrate the model and 2011 and 2013 were used to evaluate the model. Calibration consisted of estimating model genetic coefficients to minimize error between simulated and measured biomass yields. The best fit line between simulated and observed grain weight across all five N rates was 0.89 for the calibration years and 0.73 for the evaluation years. The root mean square error between simulated and measured yields was 400 kg ha-1 for the calibration years and 731 kg ha-1 for the evaluation years. The calibrated model was then run for 15 seasons of historical weather data in order to determine the marginal net return (MNR) and economic optimum N rate for the area using different N prices and the long term rice price of $0.43 kg-1. The results of the simulation gave an optimum N rate across N prices of 120-130 kg N ha-1. The model gave a relatively flat MNR response for N rates above 100 kg N ha-1 for the lowest N price, but the MNR showed a clear maximum value at 120-130 kg N ha-1 for higher N prices. Recent studies indicate that the optimum N application rate in this area is 90-120 kg N ha-1 (Zhang et al., 2009). The model results are consistent with those observed from field data. The simulated amount of N left in the soil increased substantially for N rates higher than 120-130 kg N ha-1. At high N rates and a low N price, farmers do not experience significant economic consequences for applying a higher N rate. However, on average when a farmer uses 200 kg N ha-1, they leave approximately 100 kg N ha-1 in the soil, which contaminates the groundwater. Currently, there are no economic consequences to the farmer other than the cost of N lost to the soil under higher N application rates. This may explain why the typical practice is to over-apply N. 2) Develop methods to simulate cropping systems responses across spatial scales Work was initiated this year to develop methods to simulate spatial yield variability in sugarbeets. In partnership with the University of Hohenheim (Stuttgart, Germany), we established sugarbeet plots near Stuttgart, Germany to collect baseline data needed to develop a sugarbeet model. Plots were planted in Spring, 2016 and harvested in October, 2015. Numerous biomass measurements were made including leaf number, leaf weight, leaf area index, storage root weight, nitrogen content leaves and sugar content of the beet. Data will be analyzed in 2017 and used to aid in model development. 3) Develop improvements to various DSSAT models to simulate the effects of cropping systems, management, and environment on production During this year, we initiated a project to adapt the CERES-Maize model to simulate sugarbeet growth, development and yield. Two limited datasets were available to guide model development. A comprehensive literature review was conducted and physiological algorithms were derived from published data and published relations. Preliminary algorithms were incorporated into the model. Preliminary results indicate that the model performs adequately in simulating sugarbeet growth and yield. However, we expect that additional algorithms will be developed or existing algorithms modified when we incorporate data collected in 2016 (Objective 2) into the model evaluation.

Publications