Texas ranks first in the United States in the variety and frequency of most natural disasters, such as flooding, wildfires, hurricanes, winter storms, and droughts. In February 2021, the winter storm named Uri caused an abnormal decline in the air temperature in the southcentral United States, notably in Texas. Right before Uri, most of Texas was going through a drought spell. Thus, this study analyzed how Uri influenced the drought severity, soil profile moisture content, and vegetation cover (Normalized Difference Vegetation Index, NDVI) across Texas. Data used in this analysis was obtained from the web-based geospatial applications gridMET and Crop-CASMA. The collected datasets include the Palmer Drought Severity Index (PDSI), Snow Water Equivalent (SWE), soil moisture, and NDVI at different spatial resolutions. These datasets were aggregated to the county scale using the zonal statistics analysis. The strength of the correlation between SWE and soil moisture was quantified based on the Pearson correlation coefficient. The percentage change in live vegetation cover due to the impact of the frigid temperature and snow coverage across the state was quantified by analyzing the average weekly NDVI before and after the winter storm. There was a reasonably strong correlation between the SWE contribution of Uri and the increase of the rootzone soil moisture (Pearson's r = 0.42). Similarly, the SWE showed a higher correlation with daily rootzone soil moisture with a Pearson's correlation coefficient of 0.49 on March 1. Furthermore, our results revealed a reduction in the NDVI values to less than 0.60 across Texas during the third week of February. Overall, Texas NDVI values seriously decreased due to Uri. Despite its disruptive effects on the state infrastructures and the economy, Uri snow lessened the drought conditions relatively for a short time.

A field study was conducted to investigate the effects of selected climate-smart agriculture practices on soil bulk density (ρ), porosity (β), hydraulic conductivity (K_sat), and nutrient dynamics in southeast Texas. Treatment combinations of two types of organic manure (chicken and dairy) with three rates (0, 224, and 448 kg N ha⁻¹) and two levels of biochar (2500 and 5000 kg ha⁻¹) were used in a factorial randomized block design. Bulk density and porosity measurements were conducted on undisturbed soil core samples collected from the topsoil (0–10 cm) of a field cultivated with sweet corn. K_sat was calculated from the steady-state infiltration measured using the Tension Infiltrometer (TI). The ANOVA results indicated that the manure application rates, and biochar levels significantly affected the soil properties. Compared to the control, β increased by 15% and 29% for the recommended and double recommended manure rates. Similarly, hydraulic conductivity increased by 25% in the double-recommended rate plots compared to the control. Also, we applied the concept of non-parametric elasticity to understand the sensitivity of soil physical and chemical properties to K_sat. ρ and β are critical physical properties that are highly sensitive to K_sat. Among soil nutrients, Boron showed the highest sensitivity to K_sat. Hydraulic conductivity can be enhanced by employing selected climate-smart practices and improving water management. Future directions for this study focus on scaling these findings to diverse cropping systems and soil types while integrating long-term assessments to evaluate the cumulative effects of climate-smart practices on soil health, crop productivity, and ecosystem sustainability.

Machine learning (ML) models are widely used to analyze the spatiotemporal impacts of agricultural practices on environmental sustainability, including the contribution to global greenhouse gas (GHG) emissions. Management practices, such as organic amendment applications, are critical pillars of Climate-smart agriculture (CSA) strategies that mitigate GHG emissions while maintaining adequate crop yields. This study investigated the critical threshold of soil moisture level associated with soil CO₂ emissions from organically amended plots using the classification and regression tree (CART) algorithm. Also, the study predicted the short-term soil CO₂ emissions from organically amended systems using soil moisture and weather variables (i.e., air temperature, relative humidity, and solar radiation) using multilinear regression (MLR) and generalized additive models (GAMs). The different organic amendments considered in this study are biochar (2268 and 4536 kg ha⁻¹) and chicken and dairy manure (0, 224, and 448 kg N/ha) under a sweet corn crop in the greater Houston area, Texas. The results of the CART analysis indicated a direct link between soil moisture level and the magnitude of CO₂ flux emission from the amended plots. A threshold of 0.103 m³m⁻³ was calculated for treatment amended by biochar level I (2268 kg ha⁻¹) and chicken manure at the N recommended rate (CXBX), indicating that if the soil moisture is less than the 0.103 m³m⁻³ threshold, then the median soil CO₂ emission is 142 kg ha⁻¹ d⁻¹. Furthermore, applying biochar at a rate of 4536 kg ha⁻¹ reduced the soil CO₂ emissions by 14.5% compared to the control plots. Additionally, the results demonstrate that GAMs outperformed MLR, exhibiting the highest performance under the combined effect of chicken and biochar. We conclude that quantifying soil moisture thresholds will provide valuable information for the sustainable mitigation of soil CO₂ emissions.

Sorghum [Sorghum bicolor (L.) Moench] is a vital cereal crop in semi-arid regions, but its productivity is often limited by water scarcity and inefficient nitrogen (N) management. While previous studies have explored the individual effects of irrigation and N application, limited research has examined their combined influence on sorghum growth, phenology, and yield. This study aimed to assess the impacts of four irrigation levels (0, 75, 100, and 125% of irrigation as I₀, I₇₅, I₁₀₀, and I₁₂₅) and four N application rates (0, 90, 180, and 360 kg N ha⁻¹ as N₀, N₉₀, N₁₈₀, and N₃₆₀) on sorghum performance to identify optimal water and nutrient management strategies. A field experiment was conducted over two growing seasons, measuring plant height, number of leaves, panicle length, flowering percentage, visual maturity rating, biomass, and grain yield using a split-plot design, with irrigation as the main factor and N rates as subplots with three replications. Key findings revealed that irrigation and N application significantly influenced plant height, number of leaves, panicle length, biomass, and grain yield. The highest plant height (58.06 cm), number of leaves (10.25), and panicle length (26.60 cm) were observed under I₁₂₅. Compared to the I₀ treatment, the increase in visual maturity rating in I₇₅ and I₁₀₀ was 64.91% and 249.70%, respectively. The highest biomass (3.25 t/ha) was observed for treatment I₁₂₅, while the highest grain yield (2.40 t/ha) was for I₁₀₀. Compared to N₀, treatments N₉₀, N₁₈₀, and N₃₆₀ increased grain yield by 64.8, 66.4, and 81.6%, respectively. The Pearson correlation analysis revealed significant positive relationships among key crop growth, phenology, and yield traits. The maturity rating correlated highest with flowering percentage (0.76). The response to N application was more pronounced under well-watered conditions, with the steepest grain yield increase observed under N₃₆₀. In water-limited conditions, higher N rates did not improve yield. Based on these results, applying irrigation at 100% of the crop water requirement and at least 180 kg N ha⁻¹ is recommended to enhance sorghum resilience, resource-use efficiency, and sustainable production, particularly in water-scarce regions.

Rainfall and streamflow analyses have long been central to hydrological research, yet traditional approaches often overlook the complexity introduced by changing climate signals, land-use dynamics, and human infrastructure. This study applies an integrated, data-driven framework to explore emerging hydroclimatic shifts in the Navasota River Watershed of east-central Texas. By combining autocorrelation analysis, Mann–Kendall and modified Mann–Kendall trend tests, and Pettitt’s change-point detection, we examine more than a century of precipitation and streamflow records alongside post-1978 reservoir operations. Results reveal an accelerating wetting tendency, particularly evident in decadal rolling averages and early-summer precipitation, accompanied by a statistically significant increase in 10-year moving averages of annual peak streamflow. While abrupt regime shifts were not detected, subtle but persistent changes point to evolving watershed memory and heightened flood risk in the post-dam era. This study reframes rainfall and streamflow trend analysis as a dynamic tool for anticipating hydrologic regime shifts, highlighting the urgent need for adaptive water infrastructure and flood management strategies in rapidly urbanizing and climate-sensitive watersheds.