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While space-borne optical and near-infrared facilities have succeeded in delivering a precise and spatially resolved picture of our Universe, their small survey area is known to underrepresent the true diversity of galaxy populations. Ground-based surveys have reached comparable depths but at lower spatial resolution, resulting in source confusion that hampers accurate photometry extractions. What once was limited to the infrared regime has now begun to challenge ground-based ultradeep surveys, affecting detection and photometry alike. Failing to address these challenges will mean forfeiting a representative view into the distant Universe. We introduce The Farmer : an automated, reproducible profile-fitting photometry package that pairs a library of smooth parametric models from The Tractor with a decision tree that determines the best-fit model in concert with neighboring sources. Photometry is measured by fitting the models on other bands leaving brightness free to vary. The resulting photometric measurements are naturally total, and no aperture corrections are required. Supporting diagnostics (e.g., χ ^2 ) enable measurement validation. As fitting models is relatively time intensive, The Farmer is built with high-performance computing routines. We benchmark The Farmer on a set of realistic COSMOS-like images and find accurate photometry, number counts, and galaxy shapes. The Farmer is already being utilized to produce catalogs for several large-area deep extragalactic surveys where it has been shown to tackle some of the most challenging optical and near-infrared data available, with the promise of extending to other ultradeep surveys expected in the near future. The Farmer is available to download from GitHub ( https://github.com/astroweaver/the_farmer ) and Zenodo ( https://doi.org/10.5281/zenodo.8205817 ).

Abstract Many yield predictions in perennial bioenergy species have been made based on data collected during the establishment phase of growth or a limited number of long‐term studies. Few studies compare multiple perennial crops with the dominant agricultural vegetation of the landscape over long time periods. Here, we present the results of 11 years of perennial crop management on fertile agricultural soils in central Illinois, compared with conventional row crop maize/soybean (Zea mays L., Glycine max L.) production. We examined the long‐term productivity and drought susceptibility of Miscanthus x giganteus Greef et. Deu. ex. Hodkinson et Renvoize (miscanthus), Panicum virgatum L., Cave‐in‐Rock cultivar (switchgrass), and a native prairie mix, in contrast to annual maize/soybean agriculture. Long‐term yields for miscanthus and switchgrass failed to reach initial predictions made during the establishment phase; however, in miscanthus, the 11th year of production shows little progressive yield loss with age, exceeding the modeled limit for the onset of age‐related decline. Harvest timing and differences in yields from hand and machine harvests in perennial crops likely contribute to overestimates of potential yields. Application of fertilizer to mature miscanthus resulted in significant increases in yield after a severe drought, though modeled effects of management and drought in miscanthus point to a more complex mechanism for yield response.

Abstract Perennial crops have been the focus of bioenergy research and development for their sustainability benefits associated with high soil carbon (C) and reduced nitrogen (N) requirements. However, perennial crops mature over several years and their sustainability benefits can be negated through land reversion. A photoperiod‐sensitive energy sorghum (Sorghum bicolor) may provide an annual crop alternative more ecologically sustainable than maize (Zea mays) that can more easily integrate into crop rotations than perennials, such as miscanthus (Miscanthus × giganteus). This study presents an ecosystem‐scale comparison of C, N, water and energy fluxes from energy sorghum, maize and miscanthus during a typical growing season in the Midwest United States. Gross primary productivity (GPP) was highest for maize during the peak growing season at 21.83 g C m−2 day−1, followed by energy sorghum (17.04 g C m−2 day−1) and miscanthus (15.57 g C m−2 day−1). Maize also had the highest peak growing season evapotranspiration at 5.39 mm day−1, with energy sorghum and miscanthus at 3.81 and 3.61 mm day−1, respectively. Energy sorghum was the most efficient water user (WUE), while maize and miscanthus were comparatively similar (3.04, 1.75 and 1.89 g C mm−1 H2O, respectively). Maize albedo was lower than energy sorghum and miscanthus (0.19, 0.26 and 0.24, respectively), but energy sorghum had a Bowen ratio closer to maize than miscanthus (0.12, 0.13 and 0.21, respectively). Nitrous oxide (N2O) flux was higher from maize and energy sorghum (8.86 and 12.04 kg N ha−1, respectively) compared with miscanthus (0.51 kg N ha−1), indicative of their different agronomic management. These results are an important first look at how energy sorghum compares to maize and miscanthus grown in the Midwest United States. This quantitative assessment is a critical component for calibrating biogeochemical and ecological models used to forecast bioenergy crop growth, productivity and sustainability.

Abstract In the age of biofuel innovation, bioenergy crop sustainability assessment has determined how candidate systems alter the carbon (C) and nitrogen (N) cycle. These research efforts revealed how perennial crops, such as switchgrass, increase belowground soil organic carbon (SOC) and lose less N than annual crops, like maize. As demand for bioenergy increases, land managers will need to choose whether to invest in food or fuel cropping systems. However, little research has focused on the C and N cycle impacts of reverting purpose‐grown perennial bioenergy crops back to annual cropping systems. We investigated this knowledge gap by measuring C and N pools and fluxes over 2 years following reversion of a mature switchgrass stand to an annual maize rotation. The most striking treatment difference was in ecosystem respiration (ER), with the maize‐converted treatment showing the highest respiration flux of 2,073.63 (± 367.20) g C m−2 year−1 compared to the switchgrass 1,412.70 (± 28.72) g C m−2 year−1 and maize‐control treatments 1,699.16 (± 234.79) g C m−2 year−1. This difference was likely driven by increased heterotrophic respiration of belowground switchgrass necromass in the maize‐converted treatment. Predictions from the DayCent model showed it would take approximately 5 years for SOC dynamics in the converted treatment to return to conditions of the maize‐control treatment. N losses were highest from the maize‐converted treatment when compared to undisturbed switchgrass and maize‐control, particularly during the first conversion year. These results show substantial C and N losses occur within the first 2 years after reversion of switchgrass to maize. Given farmers are likely to rotate between perennial and annual crops in the future to meet market demands, our results indicate that improvements to the land conversion approach are needed to preserve SOC built up by perennial crops to maintain the long‐term ecological sustainability of bioenergy cropping systems.

Nitrogen is an essential nutrient that directly affects plant photosynthesis, crop yield, and biomass production for bioenergy crops, but excessive application of nitrogen fertilizers can cause environmental degradation. To achieve sustainable nitrogen fertilizer management for precision agriculture, there is an urgent need for nondestructive and high spatial resolution monitoring of crop nitrogen and its allocation to photosynthetic proteins as that changes over time. Here, we used visible to shortwave infrared (400–2400 nm) airborne hyperspectral imaging with high spatial (0.5 m) and spectral (3–5 nm) resolutions to accurately estimate critical crop traits, i.e., nitrogen, chlorophyll, and photosynthetic capacity (CO2-saturated photosynthesis rate, Vmax,27), at leaf and canopy scales, and to assess nitrogen deficiency on crop yield. We conducted three airborne campaigns over a maize (Zea mays L.) field during the growing season of 2019. Physically based soil-canopy Radiative Transfer Modeling (RTM) and data-driven approaches i.e. Partial-Least Squares Regression (PLSR) were used to retrieve crop traits from hyperspectral reflectance, with ground truth of leaf nitrogen, chlorophyll, Vmax,27, Leaf Area Index (LAI), and harvested grain yield. To improve computational efficiency of RTMs, Random Forest (RF) was used to mimic RTM simulations to generate machine learning surrogate models RTM-RF. The results show that prior knowledge of soil background and leaf angle distribution can significantly reduce the ill-posed RTM retrieval. RTM-RF achieved a high accuracy to predict leaf chlorophyll content (R2 = 0.73) and LAI (R2 = 0.75). Meanwhile, PLSR exhibited better accuracy to predict leaf chlorophyll content (R2 = 0.79), nitrogen concentration (R2 = 0.83), nitrogen content (R2 = 0.77), and Vmax,27 (R2 = 0.69) but required measured traits for model training. We also found that canopy structure signals can enhance the use of spectral data to predict nitrogen related photosynthetic traits, as combining RTM-RF LAI and PLSR leaf traits well predicted canopy-level traits (leaf traits × LAI) including canopy chlorophyll (R2 = 0.80), nitrogen (R2 = 0.85) and Vmax,27 (R2 = 0.82). Compared to leaf traits, we further found that canopy-level photosynthetic traits, particularly canopy Vmax,27, have higher correlation with maize grain yield. This study highlights the potential for synergistic use of process-based and data-driven approaches of hyperspectral imaging to quantify crop traits that facilitate precision agricultural management to secure food and bioenergy production.

Epidermal Growth Factor Receptor (EGFR) is overexpressed on a number of human cancers, and often is indicative of a poor outcome. Treatment of EGFR/HER2 overexpressing cancers includes monoclonal antibody therapy (cetuximab/trastuzumab) either alone or in conjunction with other standard cancer therapies. While monoclonal antibody therapy has been proven to be efficacious in the treatment of EGFR/HER2 overexpressing tumors, drawbacks include the lack of long-lasting immunity and acquired resistance to monoclonal therapy. An alternative approach is to induce a polyclonal anti-EGFR/HER2 tumor antigen response by vaccine therapy. In this phase I/II open-label study, we examined anti-tumor immunity in companion dogs with spontaneous EGFR expressing tumors. Canine cancers represent an outbred population in which the initiation, progression of disease, mutations and growth factors closely resemble that of human cancers. Dogs with EGFR expressing tumors were immunized with a short peptide of the EGFR extracellular domain with sequence homology to HER2. Serial serum analyses demonstrated high titers of EGFR/HER2 binding antibodies with biological activity similar to that of cetuximab and trastuzumab. Canine antibodies bound both canine and human EGFR on tumor cell lines and tumor tissue. CD8 T cells and IgG deposition were evident in tumors from immunized dogs. The antibodies inhibited EGFR intracellular signaling and inhibited tumor growth in vitro. Additionally, we illustrate objective responses in reducing tumors at metastatic sites in host animals. The data support the approach of amplifying anti-tumor immunity that may be relevant in combination with other immune modifying therapies such as checkpoint inhibitors.