<p dir="ltr">Digital soil mapping (DSM) combines field and laboratory data with environmental factors to predict soil properties. The accuracy of these predictions depends on factors such as model selection, data quality and quantity, and landscape characteristics. In our study, we investigated the impact of sample density and the use of various environmental covariates (ECs) including slope, topographic position index, topographic wetness index, multiresolution valley bottom flatness, and multiresolution ridge top flatness, as well as the spatial resolution of these ECs on the predictive accuracy of four predictive models; Cubist (CB), Random Forest (RF), Regression Kriging (RK), and Ordinary Kriging (OK). Our analysis was conducted at three sites in Indiana: the Purdue Agronomy Center for Research and Education (ACRE), Davis Purdue Agriculture Center (DPAC), and Southeast Purdue Agricultural Center (SEPAC). Each site had its unique soil data sampling designs, management practices, and topographic conditions. The primary focus of this study was to predict the spatial distribution of soil properties, including soil organic matter (SOM), cation exchange capacity (CEC), and clay content, at different depths (0-10cm, 0-15cm, and 10-30cm) by utilizing five environmental covariates and four spatial resolutions for the ECs (1-1.5 m, 5 m, 10 m, and 30 m).</p><p dir="ltr">Various evaluation metrics, including R², root mean square error (RMSE), mean square error (MSE), concordance coefficient (pc), and bias, were used to assess prediction accuracy. Notably, the accuracy of predictions was found to be significantly influenced by the site, sample density, model type, soil property, and their interactions. Sites exhibited the largest source of variation, followed by sampling density and model type for predicted SOM, CEC, and clay spatial distribution across the landscape.</p><p dir="ltr">The study revealed that the RF model consistently outperformed other models, while OK performed poorly across all sites and properties as it only relies on interpolating between the points without incorporating the landscape characteristics (ECs) in the algorithm. Increasing sample density improved predictions up to a certain threshold (e.g., 66 samples at ACRE for both SOM and CEC; 58 samples for SOM and 68 samples for CEC at SEPAC), beyond which the improvements were marginal. Additionally, the study highlighted the importance of spatial resolution, with finer resolutions resulting in better prediction accuracy, especially for SOM and clay content. Overall, comparing data from the two depths (0-10cm vs 10-30cm) for soil properties predications, deeper soil layer data (10-30cm) provided more accurate predictions for SOM and clay while shallower depth data (0-10cm) provided more accurate predictions for CEC. Finally, higher spatial resolution of ECs such as 1-1.5 m and 5 m contributed to more accurate soil properties predictions compared to the coarser data of 10 m and 30 m resolutions.</p><p dir="ltr">In summary, this research underscores the significance of informed decisions regarding sample density, model selection, and spatial resolution in digital soil mapping. It emphasizes that the choice of predictive model is critical, with RF consistently delivering superior performance. These findings have important implications for land management and sustainable land use practices, particularly in heterogeneous landscapes and areas with varying management intensities.</p>

<p dir="ltr">Urban agriculture has grown in popularity in recent decades, due to its ability to provide access to healthy fruits and vegetables in urban zones, as well as its importance in fostering knowledge of agriculture within communities. However, urban agriculture may struggle with unique challenges due to its proximity to urban and industrial activities, such as food safety risks due to toxic heavy metals and metalloids which may be present in urban soils in high concentrations. Heavy metals and metalloids (HM) like arsenic, cadmium, and lead are absorbed by plants from the soil, and may accumulate in the plants’ edible tissues, which are consumed by humans. Carrot (<i>Daucus carota</i> L.), in particular, hyperaccumulates these toxic heavy metals in its edible taproots, leading to food safety risks on urban farms.</p><p dir="ltr">One potential way to help address this challenge is to breed carrot varieties with low uptake of HM. In recent years, researchers have identified lines with high and low uptake in greenhouse trials and single location breeding nurseries. However, to be viable, these lines must consistently vary in HM across sites despite differences in environmental and management factors that can also greatly influence HM bioavailability and uptake. Moreover, screening for differences in HM uptake is time-consuming and expensive, and breeders need new tools to select among segregating breeding populations. By using on-farm participatory research as well as advanced phenotyping technologies, we investigate the viability of breeding carrots for HM uptake and the potential of new tools to advance these efforts in order to mitigate the risks on urban farms.</p><p dir="ltr">In the summers of 2021 and 2022, participatory on-farm trials were conducted to determine the HM risks on Indiana urban farms and to investigate the consistency of differences in HM uptake between carrot breeding lines taken from breeding trials (Chapter 2). Results of these trials indicated that while carrot genotype had an effect, there was still significant variability in carrot uptake of arsenic, cadmium and lead between farm sites and years. Results indicated significant differences between site-years, and carrot HM concentrations that correlated strongly with soil concentrations for that particular element. However, there were some site-years with low soil HM content and other soil factors expected to reduce uptake such as pH and phytoavailable zinc concentrations (such as site-year H), that had high carrot HM content. There were significant differences in carrot cadmium (Cd) and arsenic (As) content between carrot breeding lines. For instance, breeding line 3271 had a high As average concentration but low Cd average concentration, while breeding lines 6220 and 2327 had low As and high Cd concentrations. We identify the possibility of other mediating factors, such as uptake of antagonistic micronutrients, or microbe-assisted HM uptake and amelioration that need further attention.</p><p dir="ltr">In the fall of 2022, a study was conducted to investigate the possibility of using phenotyping technologies such as RGB and hyperspectral imaging to detect Cd stress in carrot and attempt to predict uptake (Chapter 3). RGB (red green blue) is a digital color model in which cameras can capture important visual cues compiled from information about each pixel. Hyperspectral imaging uses cameras to capture wavelengths beyond the visible spectrum, which can detect plant stress indicators like increased anthocyanin content for specific environmental stresses. Results of this trial were useful, with some time points and indices noting differences between carrot lines. For instance, RGB factors hue and fluorescence as well as hyperspectral reflectance plots and vegetative indices swirNDVI and ANTH were the most diagnostic. Breeding lines 6636 and 8503 showed the greatest separation between Cd treated and control carrots in imaging indices. However, further studies will be needed to optimize this approach for breeding programs.</p><p dir="ltr">This research demonstrates that growing carrots on most urban farms in Indiana is safe. The studies also provide further evidence that it will be possible to help lower food safety risks by selecting carrot varieties with low HM uptake, and phenotyping can help to advance these efforts. At the same time, new research to understand how soil factors such as microbiomes influence HM bioavailability and uptake on urban farms are also needed to further reduce potential risks. In the meantime, farmers should continue to test their soil for HM and take appropriate actions to reduce risks such as using raised beds and soil amendments that can bind metals like biochar. Consumers should also continue to wash and peel their carrots before consumption, as well as consume a balanced diet with a diverse set of vegetables and other crops.</p>