Kaiyu Guan’s research while affiliated with University of Illinois, Urbana-Champaign and other places

Tile drainage removes excess water and is an essential, widely adopted management practice to enhance crop productivity in the U.S. Midwest. Tile drainage has been shown to significantly change hydrological and biogeochemical cycles by lowering the water table and reducing the residence time of soil water, although such impacts and their connections are poorly understood and highly uncertain. Understanding these impacts is essential, particularly so because tile drainage has been highlighted as an adaptation under projected wetter springs and drier summers in the changing climate in the U.S. Midwest. We used the ecosys model, uniquely incorporating soil oxygen dynamics and crop oxygen uptake, to quantify the impacts of tile drainage on hydrological and biogeochemical cycles and crop growth at corn-soybean rotation fields. Tiles are represented as a water sink in the soil, characterized by tile depth and spacing in ecosys. Water flow from saturated soil layers to tiles is governed by the lateral hydraulic gradient defined by the water table depth in the field, tile depth, and tile spacing. The model was validated with data from a multi-treatment, multi-year experiment in Washington, IA. The relative root mean square error (rRMSE) for corn and soybean yield in validation is 5.66 % and 12.57 %, respectively. The Pearson coefficient (r) of the monthly tile flow during the growing season is 0.78. Model results show that tile drainage reduces soil water content and enhances soil oxygenation. It additionally increases subsurface discharge and elevates inorganic nitrogen leaching, with seasonal variations influenced by climate and crop phenology. The improved aerobic condition alleviated crop oxygen stress during wet springs, thereby promoting crop root growth during the early growth stage. The development of greater root density, in turn, mitigated water stress during dry summers, leading to an overall increase in crop yield by ~6 %. These functions indicate the potential of tile drainage in bolstering crop resilience to climate change, and the use of this modeling tool for large-scale assessments of tile drainage. The model reveals the inherent connections of tile drainage’s impacts on hydrology, soil biogeochemistry, and plant growth.

The ability to automatically build 3D digital twins of plants from images has countless applications in agriculture, environmental science, robotics, and other fields. However, current 3D reconstruction methods fail to recover complete shapes of plants due to heavy occlusion and complex geometries. In this work, we present a novel method for 3D reconstruction of agricultural crops based on optimizing a parametric model of plant morphology via inverse procedural modeling. Our method first estimates depth maps by fitting a neural radiance field and then employs Bayesian optimization to estimate plant morphological parameters that result in consistent depth renderings. The resulting 3D model is complete and biologically plausible. We validate our method on a dataset of real images of agricultural fields, and demonstrate that the reconstructions can be used for a variety of monitoring and simulation applications.

Near-infrared reflectance of vegetation multiplied by incoming sunlight (NIRvP) is important for gross primary production (GPP) estimation. While NIRvP is a useful indicator of canopy structure and solar radiation, its association with heat or moisture stress is not fully understood. Thus, this research aimed to explore the impact of air temperature (Ta) and vapor pressure deficit (VPD) on the NIRvP-GPP relationship. Using Moderate Resolution Imaging Spectroradiometer (MODIS) observations, eddy-covariance measurements, and the Parameter Elevation Regressions on Independent Slopes Model (PRISM) data, we found that NIRvP cannot fully explain the response of plant photosynthesis to Ta and VPD at both seasonal and daily scales. Therefore, we incorporated a polynomial function of Ta and an exponential function of VPD to correct its seasonal response to stress and calibrated the GPP residual via a linear function of Ta and VPD time-varying derivatives to account for its daily response to stress. Leave-one-site-out cross-validation suggested that the improvements relative to its original version were especially noteworthy under stress conditions while less significant when there was no water or heat stress across grasslands and croplands. When compared to six other GPP models, the enhanced NIRvP model consistently outperformed them or performed comparably with the best model in terms of bias, RSME, and coefficient of determinant against measurements in grasslands and croplands. Moreover, we found that parameterizing the fraction of photosynthetically active radiation term using NIRv notably improved the performance of the classic MOD17 and vegetation photosynthesis model, with an average RMSE reduction of 13 % across grasslands and croplands. Overall, this study highlights the need to consider environmental stressors for improved NIRvP-based GPP and shed light on future improvements of LUE models.

Farmers in the United States have rapidly expanded the use of cover crops (CC), with national CC area nearly doubling since 2012. Despite many benefits that motivate public subsidies, questions remain about potential downsides. Using satellite observations from over 100,000 fields, half of which recently adopted CC, we demonstrate that CC led to: (i) declines in average yields for corn and soybean, by ~3% and ~2%, respectively; (ii) delays in sowing of corn (4 days) and soybean (3 days); (iii) reduced damages in the wet spring of 2019, with CC fields only half as likely to experience prevented planting as non-CC fields. CC appears to reduce important aspects of farmer risk in wet conditions but increase them in dry conditions. Timely planting of the cash crop deserves emphasis moving forward, as we show eliminating sowing delays would reduce yield penalties by roughly 50% for corn and 90% for soybean.

Information on planting dates is crucial for modeling crop development, analyzing crop yield, and evaluating the effectiveness of policy-driven planting windows. Despite their high importance, field-level planting date datasets are scarce. Satellite remote sensing provides accurate and cost-effective solutions for detecting crop phenology from moderate to high resolutions, but remote sensing-based crop planting date detection is rare. Here, we aimed to generate field-level crop planting date maps by taking advantage of satellite remote sensing-derived pheno-logical metrics and proposed a two-step framework to predict crop planting dates from these metrics using required growing degree dates (RGDD) as a bridge. Specifically, we modeled RGDD from the planting date to the spring inflection date (derived from phenological metrics) and then predicted the crop planting dates based on phenological metrics, RGDD, and environmental variables. The~3-day and 30-m Harmonized Landsat and Sentinel-2 (HLS) products were used to derive crop phenological metrics for corn and soybean fields in the U.S. Midwest from 2016 to 2021, and the ground truth of field-level planting dates from USDA Risk Management Agency (RMA) reports were used for the development and validation of our proposed two-step framework. The results indicated that our framework could accurately predict field-level planting dates from HLS-derived phenological metrics, capturing 77 % field-level variations for corn (mean absolute error, MAE=4.6 days) and 71 % for soybean (MAE=5.4 days). We also evaluated the predicted planting dates with USDA National Agricultural Statistics Service (NASS) state-level crop progress reports, achieving strong consistency with median planting dates for corn (R 2 =0.90, MAE=2.7 days) and soybeans (R 2 =0.87, MAE=2.5 days). The model's performance degraded slightly when predicting planting dates for fields with irrigation (MAE=5.4 days for corn, MAE=6.1 days for soybean) and cover cropping (MAE=5.4 days for corn, MAE=5.6 days for soybean). The USDA RMA Common Crop Insurance Policy (CCIP) provides county-or sub-county-level crop planting windows, which drive producers' decisions on when to plant. Within the CCIP-driven planting windows, higher prediction accuracies were achieved (MAE for corn: 4.5 days, soybean: 5.2 days). Our proposed two-step framework (phenological metrics-RGDD-planting dates) also outperformed the traditional one-step model (phenological metrics-planting dates). The proposed framework can be beneficial for deriving planting dates from current and future phenological products and contribute to studies related to planting dates such as the analysis of yield gaps, management practices, and government policies.

Mitigating agricultural soil greenhouse gas (GHG) emissions can contribute to meeting the global climate goals. High spatial and temporal resolution, large-scale, and multi-year data are necessary to characterize and predict spatial patterns of soil GHG fluxes to establish well-informed mitigation strategies, but not many of such datasets are currently available. To address this gap in data we collected two years of high spatial resolution (7.4 sampling points ha ⁻¹ over 2.0 to 5.4 ha area) in-season soil carbon dioxide (CO 2 ) and nitrous oxide (N 2 O) fluxes from three commercial sites in central Illinois, one conventionally managed continuous corn and two under conservation practices in corn-soybean rotations typical of the region. At the field-scale, the spatial variability of CO 2 was comparable across sites, years, and management practices, but N 2 O was on average 77% more spatially variable in the conventionally managed site. Analysis of N 2 O hotspots revealed that although they represent a similar proportion of the sampling areas across sites (conventional: 12%; conservation: 13%), hotspot contribution to field-wide emission was greater in the conventional site than in the conservation sites (conventional: 51%; conservation: 34%). Also, the spatial patterns, especially hotspot locations, of both gases were inter-annually inconsistent, with hotspots rarely occurring in the same location. Overall, our result indicated that traditional field-scale monitoring with gas chambers may not be the optimal approach to detect GHG hotspots in row crop systems, due to the unpredictable spatial heterogeneity of management practices. Still, our sensitivity analysis on the dataset demonstrated that sampling at a spatial resolution of 1.6 and 5.6 points ha ⁻¹ can provide reliable (< 25% error) estimates of field-scale soil CO 2 and N 2 O fluxes, respectively.