@article{zhou_2024, title={Potential CO<sub>2</sub> capture via enhanced weathering by basaltic sand spreading on golf courses in the US}, volume={131}, ISSN={["1878-0148"]}, DOI={10.1016/j.ijggc.2023.104032}, abstractNote={Silicate minerals weathering converts atmospheric CO2 to bicarbonate that stored in water. Golf courses regularly apply quartz-dominated sand as typical turfgrass management practice, and replacing quartz-dominated sand with weatherable silicate minerals holds the potential to provide CO2 sequestration benefits. This study reviewed the existing research and estimated net CO2 sequestration potential in scenarios involving the application of basaltic sand on golf courses. Basalt dissolution rate was strongly influenced by temperature, soil pH and grain size. Transportation of basalt material between quarry and golf course accounted for the majority of CO2 emissions, offsetting the potential CO2 sequestration benefits of enhanced basalt weathering, even when the source was located nearby. When environment favors, a 18-hole golf course in regions with an average temperature is greater than 25 °C and soil pH is approximately 8.5, the net CO2 sequestration potential achieved by substituting currently used quartz sand with basaltic sand on putting greens was a minimum 0.42 Mt CO2(eq) yr−1. Expanding this substitution to encompass the entire maintained turfgrass area, including putting greens, fairways, tees, practice areas, and roughs, resulted in a minimum 11.56 Mt CO2(eq) yr−1. Reducing the particle size to 50 μm or less resulted in a greater net CO2 sequestration potential.}, journal={INTERNATIONAL JOURNAL OF GREENHOUSE GAS CONTROL}, author={Zhou, Qiyu}, year={2024}, month={Jan} }
 @article{zhou_soldat_ruark_2024, title={Short-term soil carbon mineralization on golf course sand-based putting green and its effect on creeping bentgrass nitrogen uptake}, volume={1}, ISSN={["1435-0653"]}, DOI={10.1002/csc2.21169}, abstractNote={Abstract}, journal={CROP SCIENCE}, author={Zhou, Qiyu and Soldat, Douglas J. and Ruark, Matthew D.}, year={2024}, month={Jan} }
 @article{petelewicz_zhou_schiavon_macdonald_schumann_boyd_2024, title={Simulation-based nozzle density optimization for maximized efficacy of a machine vision-based weed control system for applications in turfgrass settings}, volume={38}, ISSN={["1550-2740"]}, DOI={10.1017/wet.2024.7}, abstractNote={Abstract Targeted spray application technologies have the capacity to drastically reduce herbicide inputs, but to be successful, the performance of both machine vision (MV) based weed detection and actuator efficiency need to be optimized. This study assessed 1) the performance of spotted spurge recognition in ‘Latitude 36’ bermudagrass turf canopy using the You Only Look Once (YOLOv3) real-time multi-object detection algorithm, and 2) the impact of various nozzle densities on model efficiency and projected herbicide reduction under simulated conditions. The YOLOv3 model was trained and validated with a dataset of 1,191 images. The simulation design consisted of 4 grid matrix regimes (3 × 3, 6 × 6, 12 × 12, and 24 × 24), which would then correspond to 3, 6, 12, and 24 non-overlapping nozzles, respectively, covering a 50-cm wide band. Simulated efficiency testing was conducted using 50 images containing predictions (labels) generated with the trained YOLO model and, by applying each of the grid matrixes to individual images. The model resulted in prediction accuracy of a F 1 Score of 0.62 precision of 0.65 and recall value of 0.60. Increased nozzle density (from 3 to 12) improved actuator precision and predicted herbicide-use efficiency with a reduction in false hits ratio from ∼ 30% to 5%. The area required to ensure herbicide deposition to all spotted spurge detected within images was reduced to 18% resulting in ∼ 80% herbicide savings compared to broadcast application. Slightly greater precision was predicted with 24 nozzles, but not statistically different from the 12-nozzle scenario. Using this turf/weed model as a basis, optimal actuator efficacy and herbicide savings would occur by increasing nozzle density from one to 12 nozzles with the context of a single band.}, journal={WEED TECHNOLOGY}, author={Petelewicz, Pawel and Zhou, Qiyu and Schiavon, Marco and Macdonald, Gregory E. and Schumann, Arnold W. and Boyd, Nathan S.}, year={2024}, month={Feb} }
 @article{zhou_soldat_2022, title={Evaluating Decision Support Tools for Precision Nitrogen Management on Creeping Bentgrass Putting Greens}, volume={13}, url={http://dx.doi.org/10.3389/fpls.2022.863211}, DOI={10.3389/fpls.2022.863211}, abstractNote={Nitrogen (N) is the most limiting nutrient for turfgrass growth. Few tools or soil tests exist to help managers guide N fertilizer decisions. Turf growth prediction models have the potential to be useful, but the lone turfgrass growth prediction model only takes into account temperature, limiting its accuracy. This study investigated the ability of a machine learning (ML)-based turf growth model using the random forest (RF) algorithm (ML-RF model) to improve creeping bentgrass (Agrostis stolonifera) putting green management by estimating short-term clipping yield. This method was compared against three alternative N application strategies including (1) PACE Turf growth potential (GP) model, (2) an experience-based method for applying N fertilizer (experience-based method), and (3) the experience-based method guided by a vegetative index, normalized difference red edge (NDRE)-based method. The ML-RF model was built based on a set of variables including 7-day weather, evapotranspiration (ET), traffic intensity, soil moisture content, N fertilization rate, NDRE, and root zone type. The field experiment was conducted on two sand-based research greens in 2020 and 2021. The cumulative applied N fertilizer was 281 kg ha−1 for the PACE Turf GP model, 190 kg ha−1 for the experience-based method, 140 kg ha−1 for the ML-RF model, and around 75 kg ha−1 NDRE-based method. ML-RF model and NDRE-based method were able to provide customized N fertilization recommendations on different root zones. The methods resulted in different mean turfgrass qualities and NDRE. From highest to lowest, they were PACE Turf GP model, experience-based, ML-RF model, and NDRE-based method, and the first three methods produced turfgrass quality over 7 (on a scale from 1 to 9) and NDRE value over 0.30. N fertilization guided by the ML-RF model resulted in a moderate amount of fertilizer applied and acceptable turfgrass performance characteristics. This application strategy is based on the N cycle and has the potential to assist turfgrass managers in making N fertilization decisions for creeping bentgrass putting greens.}, journal={Frontiers in Plant Science}, publisher={Frontiers Media SA}, author={Zhou, Qiyu and Soldat, Douglas J.}, year={2022}, month={May} }
 @article{zhou_soldat_2022, title={Influence of foot traffic, irrigation, nitrogen fertilization, and weather factors on tissue N content in creeping bentgrass ‘Focus’}, volume={14}, url={http://dx.doi.org/10.1002/its2.88}, DOI={10.1002/its2.88}, abstractNote={Abstract}, number={1}, journal={International Turfgrass Society Research Journal}, publisher={Wiley}, author={Zhou, Qiyu and Soldat, Douglas J.}, year={2022}, month={Jun}, pages={560–564} }
 @article{zhou_soldat_2021, title={Creeping Bentgrass Yield Prediction With Machine Learning Models}, volume={12}, url={http://dx.doi.org/10.3389/fpls.2021.749854}, DOI={10.3389/fpls.2021.749854}, abstractNote={Nitrogen is the most limiting nutrient for turfgrass growth. Instead of pursuing the maximum yield, most turfgrass managers use nitrogen (N) to maintain a sub-maximal growth rate. Few tools or soil tests exist to help managers guide N fertilizer decisions. Turf growth prediction models have the potential to be useful, but the currently existing turf growth prediction model only takes temperature into account, limiting its accuracy. This study developed machine-learning-based turf growth models using the random forest (RF) algorithm to estimate short-term turfgrass clipping yield. To build the RF model, a large set of variables were extracted as predictors including the 7-day weather, traffic intensity, soil moisture content, N fertilization rate, and the normalized difference red edge (NDRE) vegetation index. In this study, the data were collected from two putting greens where the turfgrass received 0 to 1,800 round/week traffic rates, various irrigation rates to maintain the soil moisture content between 9 and 29%, and N fertilization rates of 0 to 17.5 kg ha–1applied biweekly. The RF model agreed with the actual clipping yield collected from the experimental results. The temperature and relative humidity were the most important weather factors. Including NDRE improved the prediction accuracy of the model. The highest coefficient of determination (R2) of the RF model was 0.64 for the training dataset and was 0.47 for the testing data set upon the evaluation of the model. This represented a large improvement over the existing growth prediction model (R2= 0.01). However, the machine-learning models created were not able to accurately predict the clipping production at other locations. Individual golf courses can create customized growth prediction models using clipping volume to eliminate the deviation caused by temporal and spatial variability. Overall, this study demonstrated the feasibility of creating machine-learning-based yield prediction models that may be able to guide N fertilization decisions on golf course putting greens and presumably other turfgrass areas.}, journal={Frontiers in Plant Science}, publisher={Frontiers Media SA}, author={Zhou, Qiyu and Soldat, Douglas J.}, year={2021}, month={Nov} }
 @article{zhou_bleam_soldat_2020, title={The Impact of Water Loss by Evaporation and Calcite Precipitation on the Sodium Adsorption Ratio (SAR) and an Alternative Method of Estimating the SAR of Irrigation Drainage Water}, volume={10}, url={http://dx.doi.org/10.20944/preprints202010.0187.v1}, DOI={10.20944/preprints202010.0187.v1}, abstractNote={Soil water loss by evaporation influences the sodium adsorption ratio (SAR) of irrigation drainage water. Evaporation concentrates sodium and magnesium but calcite precipitation has a more complicated effect on soluble calcium and alkalinity. Here we propose a revised sodicity hazard assessment that quantifies the impact of evaporative water loss and calcite precipitation on drainage water SAR. This paper shows sodicity hazard is determined by the initial composition of irrigation water as originally suggested by previous researchers, and provide a simple, accurate way to identify the potential sodicity hazard of any irrigation water. In particular, the initial equivalent concentration of alkalinity and calcium determine the salinization pathway followed during evaporation. If the irrigation water alkalinity exceeds soluble calcium expressed as equivalent concentrations, drainage water SAR approaches an upper limit determined by the initial relative concentration of sodium and magnesium. If irrigation water alkalinity is less than soluble calcium, drainage water SAR approaches a lower limit determined by the initial calcium, magnesium and sodium. In both cases the SAR is scaled by the square root of the concentration factor &radic;Fc quantifying soil water loss. To assess the impact of evaporation and calcite precipitation on the SAR and test the accuracy of the new sodicity hazard assessment, we evaluated data from previously published lysimeter studies. We plotted water composition boundaries for each source water, comparing these boundaries to the drainage water composition recorded in the lysimeter studies. As salinity increased by evaporation, each drainage water followed a distinct salinization path.}, journal={[]}, publisher={MDPI AG}, author={Zhou, Qiyu and Bleam, William and Soldat, Douglas}, year={2020}, month={Oct} }