{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/139856"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/139856","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Data-Driven and Process-Based Modeling Approaches for Advancing Irrigation Water and Nitrogen Management in Humid Cropping Systems","abstract":"Humid agricultural regions face frequent shifts between too little and too much soil water. Since irrigation decisions depend on the current soil water status, this variability makes it harder to know the right timing and amount of irrigation. The same swings also influence how nitrogen moves through the soil. Climate change is expected to make rainfall more variable and temperatures higher, further complicating both irrigation scheduling and nitrogen (N) management. In these settings, managers need accurate, field-specific soil water parameters and clear evidence on which irrigation–nitrogen strategies perform best under current and future weather. Chapter 2 addresses why accurate soil hydraulic parameters matter in humid systems and how soil-moisture data can provide them. Using high-frequency volumetric water content from corn and cotton fields under non-irrigated, full, and precision irrigation, an event-based hydrologic signature was used to estimate field capacity (FC) and plant extraction limit (PEL) from identifiable periods of infiltration, gravity drainage, evapotranspiration, and rewetting. Compared with two existing signature methods, the event-based approach achieved higher FC classification accuracy (75.0%–79.2%) with lower FC estimation error (1.69%–1.87%). PEL classification accuracy was also higher except at deeper sensors (>30 cm), where stable moisture made events harder to detect. These results show that soil-moisture time series can supply field-specific thresholds for irrigation scheduling in humid regions. Chapter 3 evaluates irrigation and N management strategies under changing humid climates using a calibrated SWAP-WOFOST model and observed soil-moisture, yield, nitrogen uptake and nitrate leaching data. Rainfed (Rainfed-1N), calendar (Calendar-1N), and precision irrigation with single (Precision-1N) or split (Precision-2N) N applications were compared across multiple climate scenarios for yield, N uptake, nitrate (NO₃) leaching, and irrigation water productivity (WP). Precision-2N consistently outperformed Calendar-1N, with higher yields and N uptake and significantly better WP; benefits were largest when daily rainfall variability was higher and smallest when total precipitation increased. Across years, precision advantages were strongest during periods with frequent extreme temperature events. Irrigation strategy had a larger effect than N timing: Precision-1N performed similarly to Precision-2N, while Calendar-1N matched or exceeded precision-treatment yields in about 22% of years but required much more irrigation and produced lower WP. Yield and NO₃ differences were not always statistically significant, but WP improvements were significant in all but one scenario. Chapter 4 examines how modeled outcomes change between definitions of the upper limit of plant-available water (PAW). In many applications, PAW's upper limit is set with a fixed pressure head (e.g., −330 cm or −100 cm), while flux-based definitions use a drainage threshold (qFC) derived from internal drainage behavior. These choices affect how much water the model treats as available to roots during and after drainage and, in turn, influence simulated transpiration, yield, and NO₃ leaching. This chapter implements upper-limit definitions within SWAP-WOFOST using both pressure-head-based and flux-based PAW and quantifies their impacts on maize yield and NO₃ leaching under irrigated and rainfed conditions across historical and projected climates. It also tests whether irrigation reduces or amplifies the differences among PAW definitions. Together, these chapters provide (1) a data-driven way to obtain field-specific FC and PEL for scheduling irrigation in humid fields, (2) model-based evidence that precision irrigation improves yield, water productivity and can reduce NO₃ losses under challenging climate, and (3) a clear evaluation of how PAW upper-limit thresholds propagate into agronomic outcomes under climate variability.","abstract_html":"Humid agricultural regions face frequent shifts between too little and too much soil water. Since irrigation decisions depend on the current soil water status, this variability makes it harder to know the right timing and amount of irrigation. The same swings also influence how nitrogen moves through the soil. Climate change is expected to make rainfall more variable and temperatures higher, further complicating both irrigation scheduling and nitrogen (N) management. In these settings, managers need accurate, field-specific soil water parameters and clear evidence on which irrigation–nitrogen strategies perform best under current and future weather. Chapter 2 addresses why accurate soil hydraulic parameters matter in humid systems and how soil-moisture data can provide them. Using high-frequency volumetric water content from corn and cotton fields under non-irrigated, full, and precision irrigation, an event-based hydrologic signature was used to estimate field capacity (FC) and plant extraction limit (PEL) from identifiable periods of infiltration, gravity drainage, evapotranspiration, and rewetting. Compared with two existing signature methods, the event-based approach achieved higher FC classification accuracy (75.0%–79.2%) with lower FC estimation error (1.69%–1.87%). PEL classification accuracy was also higher except at deeper sensors (>30 cm), where stable moisture made events harder to detect. These results show that soil-moisture time series can supply field-specific thresholds for irrigation scheduling in humid regions. Chapter 3 evaluates irrigation and N management strategies under changing humid climates using a calibrated SWAP-WOFOST model and observed soil-moisture, yield, nitrogen uptake and nitrate leaching data. Rainfed (Rainfed-1N), calendar (Calendar-1N), and precision irrigation with single (Precision-1N) or split (Precision-2N) N applications were compared across multiple climate scenarios for yield, N uptake, nitrate (NO₃) leaching, and irrigation water productivity (WP). Precision-2N consistently outperformed Calendar-1N, with higher yields and N uptake and significantly better WP; benefits were largest when daily rainfall variability was higher and smallest when total precipitation increased. Across years, precision advantages were strongest during periods with frequent extreme temperature events. Irrigation strategy had a larger effect than N timing: Precision-1N performed similarly to Precision-2N, while Calendar-1N matched or exceeded precision-treatment yields in about 22% of years but required much more irrigation and produced lower WP. Yield and NO₃ differences were not always statistically significant, but WP improvements were significant in all but one scenario. Chapter 4 examines how modeled outcomes change between definitions of the upper limit of plant-available water (PAW). In many applications, PAW's upper limit is set with a fixed pressure head (e.g., −330 cm or −100 cm), while flux-based definitions use a drainage threshold (qFC) derived from internal drainage behavior. These choices affect how much water the model treats as available to roots during and after drainage and, in turn, influence simulated transpiration, yield, and NO₃ leaching. This chapter implements upper-limit definitions within SWAP-WOFOST using both pressure-head-based and flux-based PAW and quantifies their impacts on maize yield and NO₃ leaching under irrigated and rainfed conditions across historical and projected climates. It also tests whether irrigation reduces or amplifies the differences among PAW definitions. Together, these chapters provide (1) a data-driven way to obtain field-specific FC and PEL for scheduling irrigation in humid fields, (2) model-based evidence that precision irrigation improves yield, water productivity and can reduce NO₃ losses under challenging climate, and (3) a clear evaluation of how PAW upper-limit thresholds propagate into agronomic outcomes under climate variability.","abstract_has_math":false,"creators":["Budhathoki, Suman"],"institution":"Virginia Tech","degree_name":"Doctor of Philosophy","degree_level":"doctoral","degree_discipline":"Biological Systems Engineering","degree_department":"Biological Systems Engineering","school":null,"contributors":[],"advisors":[],"committee_chairs":["Shortridge, Julie Elizabeth"],"committee_members":["Frame, W. Hunter","Scott, Durelle T.","Stewart, Ryan D."],"year":2025,"date_issued":"2025-12-09","date_published":"2025-12-09","updated_at":"2026-07-22T22:19:26Z","subjects":["Hydrologic signatures","Irrigation water management","Soil hydraulic properties","Water productivity","Soil moisture dynamics","Field capacity"],"languages":["en"],"rights":["In Copyright"],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:45265"],"render_values":[{"text":"vt_gsexam:45265","href":null,"code":true}]}]},"links":{"outbound_url":"https://hdl.handle.net/10919/139856","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.committeechair","label":"Committee Chair","values":["Shortridge, Julie Elizabeth"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Frame, W. 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Since irrigation decisions depend on the current soil water status, this variability makes it harder to know the right timing and amount of irrigation. The same swings also influence how nitrogen moves through the soil. Climate change is expected to make rainfall more variable and temperatures higher, further complicating both irrigation scheduling and nitrogen (N) management. In these settings, managers need accurate, field-specific soil water parameters and clear evidence on which irrigation–nitrogen strategies perform best under current and future weather. Chapter 2 addresses why accurate soil hydraulic parameters matter in humid systems and how soil-moisture data can provide them. Using high-frequency volumetric water content from corn and cotton fields under non-irrigated, full, and precision irrigation, an event-based hydrologic signature was used to estimate field capacity (FC) and plant extraction limit (PEL) from identifiable periods of infiltration, gravity drainage, evapotranspiration, and rewetting. Compared with two existing signature methods, the event-based approach achieved higher FC classification accuracy (75.0%–79.2%) with lower FC estimation error (1.69%–1.87%). PEL classification accuracy was also higher except at deeper sensors (>30 cm), where stable moisture made events harder to detect. These results show that soil-moisture time series can supply field-specific thresholds for irrigation scheduling in humid regions. Chapter 3 evaluates irrigation and N management strategies under changing humid climates using a calibrated SWAP-WOFOST model and observed soil-moisture, yield, nitrogen uptake and nitrate leaching data. Rainfed (Rainfed-1N), calendar (Calendar-1N), and precision irrigation with single (Precision-1N) or split (Precision-2N) N applications were compared across multiple climate scenarios for yield, N uptake, nitrate (NO₃) leaching, and irrigation water productivity (WP). Precision-2N consistently outperformed Calendar-1N, with higher yields and N uptake and significantly better WP; benefits were largest when daily rainfall variability was higher and smallest when total precipitation increased. Across years, precision advantages were strongest during periods with frequent extreme temperature events. Irrigation strategy had a larger effect than N timing: Precision-1N performed similarly to Precision-2N, while Calendar-1N matched or exceeded precision-treatment yields in about 22% of years but required much more irrigation and produced lower WP. Yield and NO₃ differences were not always statistically significant, but WP improvements were significant in all but one scenario. Chapter 4 examines how modeled outcomes change between definitions of the upper limit of plant-available water (PAW). In many applications, PAW's upper limit is set with a fixed pressure head (e.g., −330 cm or −100 cm), while flux-based definitions use a drainage threshold (qFC) derived from internal drainage behavior. These choices affect how much water the model treats as available to roots during and after drainage and, in turn, influence simulated transpiration, yield, and NO₃ leaching. This chapter implements upper-limit definitions within SWAP-WOFOST using both pressure-head-based and flux-based PAW and quantifies their impacts on maize yield and NO₃ leaching under irrigated and rainfed conditions across historical and projected climates. It also tests whether irrigation reduces or amplifies the differences among PAW definitions. Together, these chapters provide (1) a data-driven way to obtain field-specific FC and PEL for scheduling irrigation in humid fields, (2) model-based evidence that precision irrigation improves yield, water productivity and can reduce NO₃ losses under challenging climate, and (3) a clear evaluation of how PAW upper-limit thresholds propagate into agronomic outcomes under climate variability."]},{"key":"dc:description.abstractgeneral","label":"General Abstract","values":["Farmers in humid regions often face both dry spells and heavy rain in the same season. That makes it hard to decide when to irrigate and how to manage nitrogen so that crops get what they need without wasting water or losing nutrients to the environment. With climate change expected to bring more variable rainfall and higher temperatures, there is a growing need for field-specific soil-water information and clear guidance on which practices work best. Chapter 2 addresses the need for better field information. We used soil-moisture sensors in corn and cotton fields to read the patterns that follow rain or irrigation and the drying that comes afterward. From these patterns we estimated two practical thresholds used to guide irrigation: the point when soil has drained but still holds water for plants (field capacity) and the point when plants begin to struggle to pull water (plant extraction limit). The event-based method we developed was more accurate than earlier approaches and shows a straightforward way to use sensor data to support irrigation decisions in humid areas. Chapter 3 evaluates management options under present and future weather. Using a calibrated process-based model (SWAP-WOFOST) with field measurements from Virginia, we compared no irrigation, a fixed calendar schedule, and sensor-guided precision irrigation, each paired with either a single or a split nitrogen application. Across several climate scenarios, precision irrigation generally produced more crop per unit of irrigation water and often higher yields and nitrogen uptake than the calendar method. The advantages were largest when day-to-day rainfall was more erratic and smaller when total rainfall increased. Changing how we irrigate mattered more than splitting nitrogen: a single well-timed application under precision irrigation performed about as well as a split application. In some years the calendar schedule matched yields, but it used much more water to do so. Chapter 4 examines a basic assumption built into many tools and models: how we set the upper limit of \"plant-available water.\" Some approaches treat this limit as a fixed soil condition; others define it by how slowly the soil is draining. These choices change how much water is considered usable by roots and can shift predictions of plant water use, yield, and nitrate losses. We test these contrasting definitions side by side in SWAP-WOFOST for corn under both rainfed and irrigated conditions and across historical and future weather, and we assess whether irrigation reduces or magnifies the differences among them. Together, this work shows how to pull reliable irrigation thresholds from sensor data, when and why precision irrigation pays off in humid climates, and why careful definitions of plant-available water matter for trustworthy recommendations as conditions change."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Doctor of Philosophy"]},{"key":"dc:format.medium","label":"Dc Format Medium","values":["ETD"]},{"key":"dc:title","label":"Title","values":["Data-Driven and Process-Based Modeling Approaches for Advancing Irrigation Water and Nitrogen Management in Humid Cropping Systems"]}]}],"canonical_facts":{"dc:contributor.committeechair":["Shortridge, Julie Elizabeth"],"dc:contributor.committeemember":["Frame, W. Hunter","Scott, Durelle T.","Stewart, Ryan D."],"dc:contributor.department":["Biological Systems Engineering"],"dc:creator":["Budhathoki, Suman"],"dc:date.accessioned":["2025-12-10T09:00:24Z"],"dc:date.available":["2025-12-10T09:00:24Z"],"dc:date.issued":["2025-12-09"],"dc:description.abstract":["Humid agricultural regions face frequent shifts between too little and too much soil water. Since irrigation decisions depend on the current soil water status, this variability makes it harder to know the right timing and amount of irrigation. The same swings also influence how nitrogen moves through the soil. Climate change is expected to make rainfall more variable and temperatures higher, further complicating both irrigation scheduling and nitrogen (N) management. In these settings, managers need accurate, field-specific soil water parameters and clear evidence on which irrigation–nitrogen strategies perform best under current and future weather. Chapter 2 addresses why accurate soil hydraulic parameters matter in humid systems and how soil-moisture data can provide them. Using high-frequency volumetric water content from corn and cotton fields under non-irrigated, full, and precision irrigation, an event-based hydrologic signature was used to estimate field capacity (FC) and plant extraction limit (PEL) from identifiable periods of infiltration, gravity drainage, evapotranspiration, and rewetting. Compared with two existing signature methods, the event-based approach achieved higher FC classification accuracy (75.0%–79.2%) with lower FC estimation error (1.69%–1.87%). PEL classification accuracy was also higher except at deeper sensors (>30 cm), where stable moisture made events harder to detect. These results show that soil-moisture time series can supply field-specific thresholds for irrigation scheduling in humid regions. Chapter 3 evaluates irrigation and N management strategies under changing humid climates using a calibrated SWAP-WOFOST model and observed soil-moisture, yield, nitrogen uptake and nitrate leaching data. Rainfed (Rainfed-1N), calendar (Calendar-1N), and precision irrigation with single (Precision-1N) or split (Precision-2N) N applications were compared across multiple climate scenarios for yield, N uptake, nitrate (NO₃) leaching, and irrigation water productivity (WP). Precision-2N consistently outperformed Calendar-1N, with higher yields and N uptake and significantly better WP; benefits were largest when daily rainfall variability was higher and smallest when total precipitation increased. Across years, precision advantages were strongest during periods with frequent extreme temperature events. Irrigation strategy had a larger effect than N timing: Precision-1N performed similarly to Precision-2N, while Calendar-1N matched or exceeded precision-treatment yields in about 22% of years but required much more irrigation and produced lower WP. Yield and NO₃ differences were not always statistically significant, but WP improvements were significant in all but one scenario. Chapter 4 examines how modeled outcomes change between definitions of the upper limit of plant-available water (PAW). In many applications, PAW's upper limit is set with a fixed pressure head (e.g., −330 cm or −100 cm), while flux-based definitions use a drainage threshold (qFC) derived from internal drainage behavior. These choices affect how much water the model treats as available to roots during and after drainage and, in turn, influence simulated transpiration, yield, and NO₃ leaching. This chapter implements upper-limit definitions within SWAP-WOFOST using both pressure-head-based and flux-based PAW and quantifies their impacts on maize yield and NO₃ leaching under irrigated and rainfed conditions across historical and projected climates. It also tests whether irrigation reduces or amplifies the differences among PAW definitions. Together, these chapters provide (1) a data-driven way to obtain field-specific FC and PEL for scheduling irrigation in humid fields, (2) model-based evidence that precision irrigation improves yield, water productivity and can reduce NO₃ losses under challenging climate, and (3) a clear evaluation of how PAW upper-limit thresholds propagate into agronomic outcomes under climate variability."],"dc:description.abstractgeneral":["Farmers in humid regions often face both dry spells and heavy rain in the same season. That makes it hard to decide when to irrigate and how to manage nitrogen so that crops get what they need without wasting water or losing nutrients to the environment. With climate change expected to bring more variable rainfall and higher temperatures, there is a growing need for field-specific soil-water information and clear guidance on which practices work best. Chapter 2 addresses the need for better field information. We used soil-moisture sensors in corn and cotton fields to read the patterns that follow rain or irrigation and the drying that comes afterward. From these patterns we estimated two practical thresholds used to guide irrigation: the point when soil has drained but still holds water for plants (field capacity) and the point when plants begin to struggle to pull water (plant extraction limit). The event-based method we developed was more accurate than earlier approaches and shows a straightforward way to use sensor data to support irrigation decisions in humid areas. Chapter 3 evaluates management options under present and future weather. Using a calibrated process-based model (SWAP-WOFOST) with field measurements from Virginia, we compared no irrigation, a fixed calendar schedule, and sensor-guided precision irrigation, each paired with either a single or a split nitrogen application. Across several climate scenarios, precision irrigation generally produced more crop per unit of irrigation water and often higher yields and nitrogen uptake than the calendar method. The advantages were largest when day-to-day rainfall was more erratic and smaller when total rainfall increased. Changing how we irrigate mattered more than splitting nitrogen: a single well-timed application under precision irrigation performed about as well as a split application. In some years the calendar schedule matched yields, but it used much more water to do so. Chapter 4 examines a basic assumption built into many tools and models: how we set the upper limit of \"plant-available water.\" Some approaches treat this limit as a fixed soil condition; others define it by how slowly the soil is draining. These choices change how much water is considered usable by roots and can shift predictions of plant water use, yield, and nitrate losses. We test these contrasting definitions side by side in SWAP-WOFOST for corn under both rainfed and irrigated conditions and across historical and future weather, and we assess whether irrigation reduces or magnifies the differences among them. Together, this work shows how to pull reliable irrigation thresholds from sensor data, when and why precision irrigation pays off in humid climates, and why careful definitions of plant-available water matter for trustworthy recommendations as conditions change."],"dc:description.degree":["Doctor of Philosophy"],"dc:format.medium":["ETD"],"dc:identifier.other":["vt_gsexam:45265"],"dc:identifier.uri":["https://hdl.handle.net/10919/139856"],"dc:language.iso":["en"],"dc:publisher":["Virginia Tech"],"dc:rights":["In Copyright"],"dc:rights.uri":["http://rightsstatements.org/vocab/InC/1.0/"],"dc:subject":["Hydrologic signatures","Irrigation water management","Soil hydraulic properties","Water productivity","Soil moisture dynamics","Field capacity"],"dc:title":["Data-Driven and Process-Based Modeling Approaches for Advancing Irrigation Water and Nitrogen Management in Humid Cropping Systems"],"dc:type":["Dissertation"],"thesis:degree_discipline":["Biological Systems Engineering"],"thesis:degree_level":["doctoral"],"thesis:degree_name":["Doctor of Philosophy"],"thesis:institution_name":["Virginia Polytechnic Institute and State University"]},"updated_at":"2026-07-22T22:19:26Z"}