Utility of proximal plant sensors to support nitrogen fertilization in Chrysanthemum

Chrysanthemum morifolium Ramat. is a commonly grown ornamental worldwide. A proper timing of nitrogen (N) supply is essential for a qualitative crop and the return on investment for growers. Sub-optimal nitrogen nutrition negatively influences the commercial plant quality, while supra-optimal N has an environmental impact due to nitrate leaching. Therefore, (a) reliable indicator(s) of plant nitrogen status is/are needed. Two field-grown potted Chrysanthemum cultivars, 'Maya' and 'Orlando' were studied for three consecutive years (2016-2018). Three different N treatments were applied in order to obtain a variation in N content. Plant quality measurements consisted of plant height, diameter, leaf mass per area (2017 and 2018 only), biomass and foliar and plant N content analysis. Optical measurements were performed with a SPAD sensor (2016 and 2017) and a Dualex Scientific sensor (2017 and 2018) on leaf level and with a GreenSeeker NDVI meter on canopy level. Biomass, height and diameter tended to be smaller in the minimal fertilizer treatments. Leaf mass per area did influence the relation between N and chlorophyll measured with SPAD and Dualex. Epidermal polyphenolics measured with Dualex correlated better with foliar nitrogen than non-destructive chlorophyll measurements and the nitrogen balance index. Since abaxial epidermal polyphenolics were highly correlated with foliar nitrogen and convenient to measure in-field, we propose this measurement for decision support in Chrysanthemum fertilization. Because of cultivar and sometimes year-to-year variability, reference plots can be of help for growers and advisors. NDVI was found to be more susceptible for yearly variation, but very high correlation with several quality parameters and convenience in use make this vegetation index useful for detecting the extent of spatial quality variability and thus support site dependent N requirements to reach the desired plant diameter at the end of the growing season

In-Season Yield Prediction of Cabbage with a Hand-Held Active Canopy Sensor

Efficient and precise yield prediction is critical to optimize cabbage yields and guide fertilizer application. A two-year field experiment was conducted to establish a yield prediction model for cabbage by using the Greenseeker hand-held optical sensor. Two cabbage cultivars (Jianbao and Pingbao) were used and Jianbao cultivar was grown for 2 consecutive seasons but Pingbao was only grown in the second season. Four chemical nitrogen application rates were implemented: 0, 80, 140, and 200 kg·N·ha−1. Normalized difference vegetation index (NDVI) was collected 20, 50, 70, 80, 90, 100, 110, 120, 130, and 140 days after transplanting (DAT). Pearson correlation analysis and regression analysis were performed to identify the relationship between the NDVI measurements and harvested yields of cabbage. NDVI measurements obtained at 110 DAT were significantly correlated to yield and explained 87–89% and 75–82% of the cabbage yield variation of Jianbao cultivar over the two-year experiment and 77–81% of the yield variability of Pingbao cultivar. Adjusting the yield prediction models with CGDD (cumulative growing degree days) could make remarkable improvement to the accuracy of the prediction model and increase the determination coefficient to 0.82, while the modification with DFP (days from transplanting when GDD > 0) values did not. The integrated exponential yield prediction equation was better than linear or quadratic functions and could accurately make in-season estimation of cabbage yields with different cultivars between years

Evaluation of Phosphorus Use Efficiency in Winter Wheat Varieties and Using Optical Sensors to Predict the Maize Population (Zea Mays L.)

This dissertation includes two topics; 1) evaluation of P use efficiency in winter wheat varieties. 2) using optical sensors to predict the maize population. The objective of the first topic was to (i) simulate a soil with insoluble forms of phosphorus and to evaluate seventeen winter wheat varieties for P uptake and utilization efficiency based on the total mass of P present in the soil; (ii) screen seven wheat varieties for P use efficiency in filed experiments (iii) predict the possibility of using the normalized-difference vegetative index (NDVI) for determining P limiting conditions. For P use efficiency, several studies were conducted in the growth chamber and filed experiments. The results of greenhouse studies suggested that Gallagher and Endurance were the most efficient varieties for both P uptake efficiency and P utilization efficiency. Also, The Ok11755W was the most P utilization efficient while OK10430-2 was the most P uptake efficient. Otherwise, the Ruby Lee was among the less efficient varieties either to uptake or utilize P under greenhouse conditions. Under experimental field conditions, Duster and Endurance were found to be more efficient in extracting or utilizing more P while Ruby Lee was found to be less efficient. NDVI was also possible to estimate the P deficiency due to a strong correlation between the NDVI and yield prediction under low P conditions; this observation may be used as an indicator for P recommendation. The objective of the second topic was to identify whether there is a correlation between normalized difference vegetation index (NDVI), the Coefficient of variation (CV), and the maize population. For using optical sensors, data was collected from 76 plots located at the Agronomy Research Station (EFAW) near Stillwater, OK, and the Lake Carl Blackwell Research Station (LCB) near Bray, OK. Finding and conclusion of this study suggested that since NDVI and CV were correlated to plant population, the growth stage V4 would be the appropriate stage to predict the plant population and biomass estimation, which could be useful for producers for making replant decisions and precise estimations of replanting rates.Soil Scienc

Improving Nitrogen Management in Potatoes with Active Optical Sensors

Nitrogen (N) fertilizer rate is important for high yield and good quality of potato tubers. In this dissertation, I seek to study the response of different potato cultivars under different N fertilizer rates and how that can impact tuber quality, examine the performance of active optical sensors in improving a potato yield prediction algorithm, and evaluate the ability of active optical sensors (GreenSeeker (GS) and Crop Circle (CC)) to optimize a N recommendation algorithm that can be used by potato growers in Maine. This research was conducted at 11 sites over a period of two years (2018–2019) in Aroostook County, Maine; all sites depended on a rainfed system. Three potato cultivars, Russet Burbank, Superior, and Shepody, were planted under six rates of N (0-280 kg ha-1), ammonium sulfate and ammonium nitrate, and were applied in a randomized complete block design (RCBD) with four replications. Active optical sensor readings (normalized difference vegetation index (NDVI)) were collected weekly after the fourth leaf stage began. The coefficient of determination (R2) between soil organic matter (OM) content and total tuber yield for all sites combined was 0.78**. Sites with ≥ 30 g kg-1 of soil OM produced higher total tuber yield, marketable yield, and tuber weight per plant (39.45%, 45.22%, and 54.94%, respectively) than sites with ≤ 30 g kg-1 of OM. Specific gravity increased by 0.18% in the sites with ≥ 30 g kg-1 of OM. The total tuber yield for the three cultivars was maximized at 168 kg N ha-1. Vegetation indices measurements obtained at stages of 16 or 20 fully expanded leaves were significantly correlated with tuber yield, which can be used in the yield prediction model. Sensor measurements obtained at the 20th leaf stage were significantly correlated with tuber yield, with the exponential model showing the best fit for the regression curve. The recommended N rate calculated based on in-season sensor readings was reduced by approximately 12–14% compared to the total N rate that growers currently apply based on the conventional approach