Modeling the effects of environmental and management variables on crop productivity

Lin, Tzu-Shun

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https://hdl.handle.net/2142/117585 Copy

Description

Title

Modeling the effects of environmental and management variables on crop productivity

Author(s)

Lin, Tzu-Shun

Issue Date

2022-12-01

Director of Research (if dissertation) or Advisor (if thesis)

Jain, Atul

Doctoral Committee Chair(s)

Jain, Atul

Committee Member(s)

• Nesbitt, Stephen
• Dominguez, Francina
• Kotamarthi, Rao
• Kheshgi, Haroon

Department of Study

Atmospheric Sciences

Discipline

Atmospheric Sciences

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

• ISAM
• WRF, CO2, climate change, corn, soybean, rice, irrigation, N fertilizer, N manure, bioenergy grasses, crop-climate interaction, cultivar

Abstract

Crop growth is influenced by changes in environmental conditions and agricultural management practices. Crop growth also influences the atmosphere and in turn, feedback on the planted crop. Understanding climate change and land management and their interactions with crops are of particular interest as they are the key to assessing future food productivity under changing climate. This dissertation aims for the productivity of various crops, including corn (maize), soybean, rice, and bioenergy grasses, and their drivers and feedback processes by developing a data-model integrated framework and applying it at the site, regional and global scales. Chapter 1 of the dissertation introduces the overall objectives and method. Chapter 2 examines the effects of the past and future changes in climate, atmospheric CO2 concentration, irrigation, heat stress, and N on global corn and soybean over the 20th and 21st centuries under two future scenarios at 0.5-degree spatial resolution. To characterize and assess corn and soybean yield, I extend and evaluate crop-specific biophysical processes and feedbacks in a land surface model, the Integrated Science Assessment Model (ISAM), including planting time, heat stress as a function of canopy temperature, seeding rates, and residue management, as well as CO2 and N fertilization, and irrigation feedback effects. With the simulation of heat stress impact by canopy temperature, irrigation reduces soil water stress and cools canopy temperature, which in turn feedbacks the crop growth during the growing season and heat stress impact during the reproductive period. Over the 21st century, the adverse warmer temperature effect on corn and soybean yield is alleviated by other drivers, mainly the increase in atmospheric CO2 concentration and resultant changes in the phenological events due to climate change, particularly planting dates and harvesting times, by 2090s under both scenarios. The results from this part have implications for understanding the potential management adaptation of N fertilizer, planting time, and irrigation on future corn and soybean yield and their interactions with environmental factors, including climate and atmospheric CO2. Chapter 3 extends the framework developed in the first part to assess historical rice yield and production at 0.5-degree spatial resolution. I have implemented a rice growth module in ISAM, which accounts for flooded irrigation, transplanting, multiple seasons, and surface water layer processes. After evaluating the model processes using the site-level observations and global datasets, the model is applied to study the historical environmental and management factors on global rice production. The model results estimated global rice yield from transplanted rice is higher than from direct-seeded rice. Still, the rice production is higher from direct-seeded due to higher cultivated areas. Among the three rice growing seasons, rice harvested in the main growing season one features the longest growing period with favorable climate conditions, resulting in the highest yield and production. The global total production of rice for 2006-2015 is 713 million tons, with South and Southeast Asia by far the largest rice-producing region, and China (24%), India (22%), and Thailand (9%) being the top three producers. The expansion of total rice harvested areas is the major contributor (56%). Other positive effects of environmental and management factors on rice production include N fertilizer and manure (23%), N deposition (2%), and rising atmospheric CO2 concentration (24%). In contrast, climate change reduced production by 5%. Chapter 4 investigates the historical climate trend and extremes and management practices (i.e., seeding rate, N fertilizer, cultivar choice, irrigation, and area expansion) on corn yield and production in the United States at 0.1-degree spatial resolution. I implement a longer growing season cultivar into ISAM by modifying corn phenology using the observation data for a longer growing season cultivar. A longer-growing season corn cultivar indicates the reproductive period has been lengthened by approximately 0.21 days per year and has contributed to 27% of the observed increase in national corn production since 1980. The model simulations capture the corn productivity loss due to extreme climate events. Specifically, the model captures yield responses to heatwaves, droughts, and floods and explains the observed national corn yield and production annual variability by 69% and 82%, respectively. Without considering spatial variations in longer-growing cultivars, the explainable annual variability for corn yield and production are reduced from 69 to 56% and 82 to 64%, respectively. Chapter 5 extends the US scale model of Chapter 4 to assess the bioethanol yield from cultivating corn and bioenergy grasses, including Miscanthus, and two different cultivars of switchgrass, Cave-in-Rock, and Alamo in the Central and Eastern United States. Bioenergy grasses require more water than corn but improve water quality due to controlling nitrogen leaching relative to corn. Growing bioenergy grasses are also less affected by 2012 Midwest extreme droughts and heatwaves events than corn because bioenergy grasses are perennial with deeper and denser roots. Therefore relatively less impacted by water stress because deeper roots can extract the water from deeper moist soils. However, the high-yield region of bioenergy crops in the southern and eastern States of the Midwest are overlapped with the corn cultivated region, implying concerns about competition with food production. Chapter 6 extends the US scale land surface model developed and applied in Chapters 4 and 5 by coupling it with WRF (Weather Research and Forecasting) model. The coupled ISAM-WRF model investigates the feedback between changes in surface energy and water fluxes and atmospheric temperature and precipitations due to crop cultivars in the Midwest United States. I use ISAM-WRF to study the effects of the above-described longer-season cultivars on the surface climate variables including, temperature and precipitation and changes in these climate conditions on corn/soybean yields. Through the land-atmospheric coupling, the long-growing cultivars change the daily mean 2-m temperature between -1.5 and 1oC and precipitation between -10 and 10 mm per day during the growing season at a Bondville, IL, cropland flux site, highlighting the agricultural management on crops can increase productivity and change the seasonal climate. The modeled precipitation, near-surface temperature, and surface energy budgets are well evaluated with observational data and show that the results capture seasonal variability of regional climate, implying the importance of developing crop productivity in numerical weather prediction and climate models to study feedback processes. Chapter 7 provides the overall summary and suggestions for possible future work related to the study in the dissertation.

Graduation Semester

2022-12

Type of Resource

Thesis

Handle URL

https://hdl.handle.net/2142/117585

Copyright and License Information

Copyright 2022 Tzu-Shun Lin

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