Assessing corn recovery from early season nutrient stress under different soil moisture regimes.

Corn (Zea mays) biomass accumulation and nutrient uptake by the six-leaf collar (V6) growth stage are low, and therefore, synchronizing nutrient supply with crop demand could potentially minimize nutrient loss and improve nutrient use efficiency. Knowledge of corn's response to nutrient stress in the early growth stages could inform such nutrient management. Field studies were conducted to assess corn recovery from when no fertilizer application is made until the V6 growth stage, and thereafter, applying fertilizer rates as those in non-stressed conditions. The early season nutrient stress and non-stress conditions received the same amount of nutrients. As the availability of nutrients for plant uptake is largely dependent on soil moisture, corn recovery from the early season nutrient stress was assessed under different soil moisture regimes induced via irrigation scheduling at 50% and 80% field capacity under overhead and subsurface drip irrigation (SSDI) systems. Peanut (Arachis hypogaea) was the previous crop under all conditions, and the fields were under cereal rye (Secale cereale) cover crop prior to planting corn. At the V6 growth stage, the nutrient concentrations of the early season-stressed crops, except for copper, were above the minimum threshold of sufficiency ranges reported for corn. However, the crops showed poor growth, with biomass accumulation being reduced by over 50% compared to non-stressed crops. Also, the uptake of all nutrients was significantly lower under the early season nutrient stress conditions. The recovery of corn from the early season nutrient stress was low. Compared to non-stress conditions, the early season nutrient stress caused 1.58 Mg ha-1 to 3.4 Mg ha-1 yield reduction. The percent yield reduction under the SSDI system was 37.6-38.2% and that under the overhead irrigation system was 11.7-13%. The high yield reduction from the early season nutrient stress under the SSDI system was because of water stress conditions in the topsoil soil layer. The findings of the study suggest ample nutrient supply in the early season growth stage is critical for corn production, and thus, further studies are recommended to determine the optimum nutrient supply for corn at the initial growth stages.

Estimating yield-contributing physiological parameters of cotton using UAV-based imagery.

Lint yield in cotton is governed by light intercepted by the canopy (IPAR), radiation use efficiency (RUE), and harvest index (HI). However, the conventional methods of measuring these yield-governing physiological parameters are labor-intensive, time-consuming and requires destructive sampling. This study aimed to explore the use of low-cost and high-resolution UAV-based RGB and multispectral imagery 1) to estimate fraction of IPAR (IPARf), RUE, and biomass throughout the season, 2) to estimate lint yield using the cotton fiber index (CFI), and 3) to determine the potential use of biomass and lint yield models for estimating cotton HI. An experiment was conducted during the 2021 and 2022 growing seasons in Tifton, Georgia, USA in randomized complete block design with five different nitrogen treatments. Different nitrogen treatments were applied to generate substantial variability in canopy development and yield. UAV imagery was collected bi-weekly along with light interception and biomass measurements throughout the season, and 20 different vegetation indices (VIs) were computed from the imagery. Generalized linear regression was performed to develop models using VIs and growing degree days (GDDs). The IPARf models had R2 values ranging from 0.66 to 0.90, and models based on RVI and RECI explained the highest variation (93%) in IPARf during cross-validation. Similarly, cotton above-ground biomass was best estimated by models from MSAVI and OSAVI. Estimation of RUE using actual biomass measurement and RVI-based IPARf model was able to explain 84% of variation in RUE. CFI from UAV-based RGB imagery had strong relationship (R2 = 0.69) with machine harvested lint yield. The estimated HI from CFI-based lint yield and MSAVI-based biomass models was able to explain 40 to 49% of variation in measured HI for the 2022 growing season. The models developed to estimate the yield-contributing physiological parameters in cotton showed low to strong performance, with IPARf and above-ground biomass having greater prediction accuracy. Future studies on accurate estimation of lint yield is suggested for precise cotton HI prediction. This study is the first attempt of its kind and the results can be used to expand and improve research on predicting functional yield drivers of cotton.

Applications of Unmanned Aerial Vehicle Based Imagery in Turfgrass Field Trials.

Recent advances in remote sensing technology, especially in the area of Unmanned Aerial Vehicles (UAV) and Unmanned Aerial Systems (UASs) provide opportunities for turfgrass breeders to collect more comprehensive data during early stages of selection as well as in advanced trials. The goal of this study was to assess the use of UAV-based aerial imagery on replicated turfgrass field trials. Both visual (RGB) images and multispectral images were acquired with a small UAV platform on field trials of bermudagrass (Cynodon spp.) and zoysiagrass (Zoysia spp.) with plot sizes of 1.8 by 1.8 m and 0.9 by 0.9 m, respectively. Color indices and vegetation indices were calculated from the data extracted from UAV-based RGB images and multispectral images, respectively. Ground truth measurements including visual turfgrass quality, percent green cover, and normalized difference vegetation index (NDVI) were taken immediately following each UAV flight. Results from the study showed that ground-based NDVI can be predicted using UAV-based NDVI (R 2 = 0.90, RMSE = 0.03). Ground percent green cover can be predicted using both UAV-based NDVI (R 2 = 0.86, RMSE = 8.29) and visible atmospherically resistant index (VARI, R 2 = 0.87, RMSE = 7.77), warranting the use of the more affordable RGB camera to estimate ground percent green cover. Out of the top ten entries identified using ground measurements, 92% (12 out of 13 in bermudagrass) and 80% (9 out of 11 in zoysiagrass) overlapped with those using UAV-based imagery. These results suggest that UAV-based high-resolution imagery is a reliable and powerful tool for assessing turfgrass performance during variety trials.