Predicting Nutrient Content, Plant Health, and Site Suitability: A Case Study of Eragrostis tef

Advancements in agricultural and geographic principals have led to worldwide food and agricultural globalization. Because agricultural production continues to further in global interconnectedness, confirmed precision agriculture (PA) methods are required to monitor crops in-field. PA utilizes a remote sensing method referred to as imaging spectroscopy (IS). IS is often performed using a field spectroradiometer that identifies reflectance values. The reflectance values obtained have been utilized in agricultural studies to correlate spectral reflectance to biochemical and biophysical properties. However, while there is a large body of research focusing on IS predicting these agricultural characteristics, many studies have only employed the research in a single region/location resulting in findings that may lacking reproducibility and replication (R&R) for more than a single environment. The lack of regionally comparative IS methods for nutrient and plant health analysis is important as varying geographies may prove to have an effect on IS findings. Therefore, the proposed research utilizes IS methods to predict nutrient and plant health values utilizing tef (Eragrostis tef) as a case study as its cultivated in Ethiopia and the United States. Currently, in the United States, the cultivation of tef is limited thus the United States could benefit from an exploration of site suitability analysis to aid expansion of tef cultivation in the U.S. It is through this interdisciplinary study that potential improvement to geography and remote sensing theory/methods can be obtained to achieve goals within food/agriculture geography

Evaluation of chickpea (Cicer arietinum L.) genotypes for tolerance to Frost in controlled environment

The study aimed to evaluate the frost tolerance variability of Ethiopian chickpea (Cicer arietinum L.) germplasm under controlled environment using growth chamber. A total of 72 genotypes were screened for frost tolerance using complete randomized design with two replications. The analysis of variance result indicated that there was a significant (P<0.01) difference amongst genotypes for plant height, number of foliage, number of primary branch, growth rate, and fresh biomass weight. Based on plant survival rate (SR), 31 (43.1%) genotypes scored above 0.8 values. Based on Freezing tolerance rate (FTR), 37(51.4%) and 31(43.1%) genotypes were rated at a score of 1 to 3 in freezing test 1 (T1) and freezing test 2 (T2), respectively. There was a strong negative correlation between fresh biomass yields with SR (-0.75** for T1 and -0.71** for T2 at p<0.01), while a strong positive correlation with FTR value (0.74** at p<0.01). Based on the combined result of FTR and SR scores, 26 genotypes were found to be frost-tolerant genotypes at a temperature level as low as -5oC at seedling stage. Based on our findings, Ethiopian chickpea germplasm has a genetic potential for frost-tolerance traits for use in breeding programs

Machine learning algorithms improve MODIS GPP estimates in United States croplands

Introduction: Machine learning methods combined with satellite imagery have the potential to improve estimates of carbon uptake of terrestrial ecosystems, including croplands. Studying carbon uptake patterns across the U.S. using research networks, like the Long-Term Agroecosystem Research (LTAR) network, can allow for the study of broader trends in crop productivity and sustainability.Methods: In this study, gross primary productivity (GPP) estimates from the Moderate Resolution Imaging Spectroradiometer (MODIS) for three LTAR cropland sites were integrated for use in a machine learning modeling effort. They are Kellogg Biological Station (KBS, 2 towers and 20 site-years), Upper Mississippi River Basin (UMRB - Rosemount, 1 tower and 12 site-years), and Platte River High Plains Aquifer (PRHPA, 3 towers and 52 site-years). All sites were planted to maize (Zea mays L.) and soybean (Glycine max L.). The MODIS GPP product was initially compared to in-situ measurements from Eddy Covariance (EC) instruments at each site and then to all sites combined. Next, machine learning algorithms were used to create refined GPP estimates using air temperature, precipitation, crop type (maize or soybean), agroecosystem, and the MODIS GPP product as inputs. The AutoML program in the h2o package tested a variety of individual and combined algorithms, including Gradient Boosting Machines (GBM), eXtreme Gradient Boosting Models (XGBoost), and Stacked Ensemble.Results and discussion: The coefficient of determination (r2) of the raw comparison (MODIS GPP to EC GPP) was 0.38, prior to machine learning model incorporation. The optimal model for simulating GPP across all sites was a Stacked Ensemble type with a validated r2 value of 0.87, RMSE of 2.62 units, and MAE of 1.59. The machine learning methodology was able to successfully simulate GPP across three agroecosystems and two crops

A comprehensive overview of radioguided surgery using gamma detection probe technology

The concept of radioguided surgery, which was first developed some 60 years ago, involves the use of a radiation detection probe system for the intraoperative detection of radionuclides. The use of gamma detection probe technology in radioguided surgery has tremendously expanded and has evolved into what is now considered an established discipline within the practice of surgery, revolutionizing the surgical management of many malignancies, including breast cancer, melanoma, and colorectal cancer, as well as the surgical management of parathyroid disease. The impact of radioguided surgery on the surgical management of cancer patients includes providing vital and real-time information to the surgeon regarding the location and extent of disease, as well as regarding the assessment of surgical resection margins. Additionally, it has allowed the surgeon to minimize the surgical invasiveness of many diagnostic and therapeutic procedures, while still maintaining maximum benefit to the cancer patient. In the current review, we have attempted to comprehensively evaluate the history, technical aspects, and clinical applications of radioguided surgery using gamma detection probe technology

Nutrient Prediction for Tef (Eragrostis tef) Plant and Grain with Hyperspectral Data and Partial Least Squares Regression: Replicating Methods and Results across Environments

Achieving reproducibility and replication (R&R) of scientific results is tantamount for science to progress, and it is also necessary for ensuring the self-correcting mechanism of the scientific method. Topics of R&R have sailed to the forefront of research agenda in many fields recently but have received less attention in remote sensing in general and specifically for studies utilizing hyperspectral data. Given the extremely local environments in which many hyperspectral studies are conducted (e.g., agricultural field plots), purposeful attention to the repeatability of findings across study locales can help ensure methods are generalizable. This study undertakes an investigation of the nutrient content of tef (Eragrostis tef), an understudied plant that is growing in importance due to both food and forage benefits, but does so within the context of the replicability of methods and findings across two study sites situated in different international and environmental contexts. The aims are to (1) determine whether calcium, magnesium, and protein of both the plant and grain can be predicted using hyperspectral data with partial least squares (PLS) regression with waveband selection, and (2) compare the replicability of models across differing environments. Results suggest the method can produce high nutrient prediction accuracy for both the plant and grain in individual environments, but selection of wavebands for nutrient prediction was not comparable across study areas. The findings suggest that the method must be calibrated in each location, thereby reducing the potential to extrapolate methods to different areas. Our findings highlight the need for greater attention to methods and results replication in remote sensing, specifically hyperspectral analyses, in order for scientific findings to be repeatable beyond the plot level

Cooling effects of increased green fodder area on native grassland in the northeastern Tibetan Plateau

With increasing livestock production due to high demand for consumption, the planted area of green fodder, an essential livestock supplement, has grown rapidly and will continue to grow in China. However, the climate feedback of this rapid land cover conversion is still unclear. Using multisource data (e.g. remote sensing observation and meteorological data), we compared the land surface temperature of green fodder plantation areas and native grassland in the northeastern Tibetan Plateau. The green fodder area was detected to be cooler than the native grassland by −0.54 ± 0.98 °C in the daytime throughout the growing season. The highest magnitude (−1.20 ± 1.68 °C) of cooling was observed in August. A nonradiative process, indicated by the energy redistribution factor, dominated the cooling effects compared to the radiative process altered by albedo variation. The results indicate the potential cooling effects of increasing green fodder area on native grassland, highlighting the necessity of investigating climate feedback from anthropogenic land use change, including green fodder expansion