Accurate estimation of radiation use efficiency (RUE) at variable timed sowing dates will enhance the prediction of plant dry matter accumulation. The objectives of this study were to (1) determine the impact of three sowing dates on the productivity, performance, and economic feasibility of three biomass sorghum hybrids and (2) evaluate the variability of RUE in the production of biomass sorghum under the effects of variable timed sowing dates. Over a two-year experiment, biomass sorghum hybrids were grown and monitored at different sowing seasons under optimal growth conditions. Average dry biomass (DB) productivity at harvest ranged from 22.71 to 32.77 Mg ha⁻¹. Higher leaf area index (LAI) values (>4.0) represented an intercept of over 95% of incident photosynthetically active radiation (PAR). RUE obtained from the slope of the linear relationship between DB produced and accumulated intercepted photosynthetically active radiation (IPAR) ranged from 2.92 to 4.157 g MJ⁻¹ across growing seasons. Higher RUE values were observed for the energy hybrids in the early and mid-season. They converted IPAR efficiently into DB. Lastly, the economic feasibility of each sorghum hybrid and sowing date was evaluated in terms of their expected net returns. Economic results suggest that the sorghum hybrids considered could be a viable source of biomass season long, with net returns ranging from USD 560.55 ha⁻¹ to USD 1255.06 ha⁻¹.

Huanglongbing (HLB), also known as citrus greening disease, is a devastating disease of citrus. However, there is no known cure so far. Recently, under Section 24(c) of the Federal Insecticide, Fungicide, and Rodenticide Act (FIFRA), a special local need label was approved that allows the trunk injection of antimicrobials such as oxytetracycline (OTC) for HLB management in Florida. The objectives of this study were to use UAS-based remote sensing to assess the effectiveness of OTC on the HLB-affected citrus trees in Texas and to differentiate the levels of HLB severity and canopy health. We also leveraged UAS-based features, along with machine learning, for HLB severity classification. The results show that UAS-based vegetation indices (VIs) were not sufficiently able to differentiate the effects of OTC treatments of HLB-affected citrus in Texas. Yet, several UAS-based features were able to determine the severity levels of HLB and canopy parameters. Among several UAS-based features, the red-edge chlorophyll index (CI) was outstanding in distinguishing HLB severity levels and canopy color, while canopy cover (CC) was the best indicator in recognizing the different levels of canopy density. For HLB severity classification, a fusion of VIs and textural features (TFs) showed the highest accuracy for all models. Furthermore, random forest and eXtreme gradient boosting were promising algorithms in classifying the levels of HLB severity. Our results highlight the potential of using UAS-based features in assessing the severity of HLB-affected citrus.

Water availability and supply are critical factors in the production of bioenergy. Dry biomass productivity and water use efficiency (WUE) of two biomass sorghum cultivars (Sorghum bicolor (L.) Moench) were studied in two different climatic locations during 2014 and 2015. The objective of this field study was to evaluate the dry biomass productivity and water use efficiency of two energy sorghum cultivars grown in two different climatic environments: one at Pecos located in the Chihuahuan Desert and a second one located at Weslaco in the Lower Rio Grande bordering Mexico and with a semiarid environment. There were significant differences between locations in dry biomass and WUE. Dry biomass productivity ranged from 22.4 to 31.9 Mg ha⁻¹ in Weslaco, while in Pecos it ranged from 7.4 to 17.6 Mg ha⁻¹. Even though it was possible to produce energy sorghum biomass in an arid environment with saline-sodic soils and saline irrigation, the energy sorghum dry biomass yield was reduced more than 50% in the arid environment compared to production in a semiarid environment with good soil and water quality, and it required approximately twice as much water. Harsh production conditions combined with low energy prices resulted in negative net returns for all treatments. However, a moderate increase in ethanol price could make the semiarid cropland of Texas an economically feasible feedstock production location.

Spinach ( Spinacia oleracea L.) yield prediction using unmanned aerial systems (UAS) has shown promise, but most existing models have been trained and validated within a single trial, leaving their generalizability across growing seasons largely untested; although UAS‐based growth analysis has been applied in other crops, it remains underexplored in spinach. The objectives of this study were to assess whether the fusion of vegetation indices (VIs) and textural features (TFs) could improve prediction ability, as evaluated through a cross‐year validation, in which the model was trained on data from 1 year and tested on unseen data from a different year. In addition, the UAS feature was further leveraged to perform spinach growth analysis. A 2‐year experiment (Exp. 1 and Exp. 2) was conducted using a randomized complete block design. One UAS flight mission was carried out before harvest, and yield was manually harvested at Exp. 1, while nine flight missions were implemented in addition to four biomass subsamples for Exp. 2. The UAS data and fresh yield of spinach from Exp. 2 were used to establish machine‐learning models and perform growth analysis, while the data from Exp. 1 was used to validate model accuracy. A fusion of VIs and TFs significantly improved the prediction ability of the models validated by the cross‐year scheme. Among the four models, Lasso regression showed the highest accuracy. In addition, a significant correlation between the maximum normalized difference red edge index (NDRE) growth rate and average yield was observed, suggesting that NDRE at the early growth stages reflects the final spinach yield. Our study highlights the potential of UAS, along with machine learning and growth function, to predict yield and describe growth of spinach.