About

Professor Srinivasulu Ale leads the Agrohydrology research program at the Texas A&M AgriLife Research and Extension Center at Vernon. His research focuses on developing and evaluating climate-resilient, regenerative agricultural strategies aimed at conserving soil and water, enhancing crop water productivity, and protecting soil and water quality across diverse agroecosystems. The program employs hydrologic, ecosystem, and crop growth models alongside machine learning approaches to address challenges in crop and rangeland settings. Key research areas include assessing the hydrologic and environmental impacts of land use and grazing management changes, developing efficient irrigation strategies and decision support tools, evaluating regenerative agricultural practices on soil health and ecosystem services, and studying the effects of warming and drying climates on crop production with an emphasis on adaptation strategies. Professor Ale's work also involves identifying climate-adaptive alternative crops and forages to support sustainable agriculture under changing environmental conditions. His research projects encompass evaluating grazing management impacts on hydrology and soil and water quality at ranch and watershed scales, particularly comparing traditional continuous grazing with adaptive multi-paddock grazing practices. These studies have demonstrated that adaptive multi-paddock grazing can significantly reduce surface runoff, sediment and nutrient losses, and flood risk while improving soil organic carbon and ecosystem functions. Additionally, Professor Ale's work addresses the development and evaluation of efficient irrigation and crop management strategies for semi-arid regions like the Texas High Plains and Rolling Plains, using the DSSAT Cropping System Model to optimize irrigation timing and crop production under current and future climate scenarios. He has contributed to the creation of innovative decision support tools such as the idCROP mobile app, which integrates sensor data and crop models to provide real-time irrigation scheduling recommendations. Professor Ale's research also investigates the hydrologic and water quality impacts of biofuel-induced land use changes, reflecting the increasing demand for land for biofuel production and its implications for agricultural land use and watershed health. His comprehensive approach combines modeling, field studies, and technology development to promote sustainable agricultural practices that enhance ecosystem resilience and productivity in the face of environmental challenges.

Research topics

• Environmental science
• Biology
• Agronomy
• Economics
• Engineering
• Water resource management
• Agroforestry
• Soil science
• Ecology

Selected publications

• Impacts of a Warming and Drying Climate on Agriculture and Food Production

  2026-02-24

  book-chapterSenior author

  Texas agriculture plays an important role in the state's economy. Texas is a leading producer of cattle, poultry, important crops such as cotton, corn, sorghum, wheat, rice, and a variety of fruits and vegetables. Texan farmers sold $32.2 billion worth of agricultural products in 2022 and the food and fiber systems in the state contributed 9.1% of the state's gross domestic product (GDP) in 2021. Texas crop producers face challenges from declining water availability, recurring droughts and occasional floods, and projected warmer and drier climate and changes in rainfall patterns in the future. Crops respond differently to changes in climate because of their varying physiological characteristics. An understanding of the impacts of climate change on crop production is therefore essential for long-term planning and decision-making to ensure sustainable and resilient food and fiber production systems. This chapter discusses climate change impacts on agricultural and food production in Texas in four major sections that focus on: (1) an overview of agricultural sector in Texas and crop-wise contributions to the state economy; (2) a discussion on the effects of climate variables including air temperature, atmospheric carbon dioxide, and rainfall on the growth and development of C3 and C4 crops; (3) a review of studies that assessed climate change impacts on production of major field and forage crops, and fruits and trees in Texas; and (4) a description of potential climate change adaptation strategies including management-based adaptions, genotype-based adaptations, and potential alternative crops and cropping systems.

  PublisherDOI

• Effects of rising CO2 concentrations on water dynamics and yields for C3 and C4 crops under both irrigated and dryland conditions in the Texas High Plains

  Environmental Modelling & Software · 2026-01-06

  article
  PublisherDOI

• Optimization of parameters for the HEC-HMS model based on real-time flow monitoring data: a case study of Hyderabad metropolitan area, India

  Modeling Earth Systems and Environment · 2025-07-19 · 2 citations

  articleSenior author
  PublisherDOI

• Calibration and bias correction of seasonal weather forecasts from the North American Multi-Model Ensemble: Potential applications for regional crop modelling and irrigation management

  The Journal of Agricultural Science · 2025-02-27 · 1 citations

  articleCorresponding

  Abstract Reliable seasonal weather forecasts are essential for irrigation management and crop yield prediction, particularly in regions with limited water resources. This study aimed to improve the usability of the North American Multi-Model Ensemble (NMME), an experimental real-time seasonal weather forecast system, for regional crop modelling and irrigation decision-making. Coarse resolution of NMME may introduce bias and uncertainty at regional/local scales. To address this, a statistical downscaling method with bias correction for both mean and variability was used to produce 1-km gridded daily weather projections for temperature and precipitation across the contiguous United States from a representative NMME model, the Canadian Coupled Climate Model version 4 (CanCM4). The daily surface weather and climatological summaries (DAYMET) data were used to calibrate the downscaled hindcast projections of CanCM4. The reliability of downscaled CanCM4 forecasts for local crop modelling was evaluated at lead times of up to six months using a calibrated DSSAT model at a research station in the semi-arid Texas Rolling Plains region. Cross-validation during the hindcast period demonstrated strong forecast skill, with R 2 values of 0.72 and 0.71 for maximum and minimum temperatures, respectively. The precipitation forecast remained sensitive to extreme events, with seasonal and annual relative errors of 31 and 1 %, respectively. Crop yield predictions had a relative error of 9 %, and irrigation water requirements closely matched field observations, outperforming both raw CanCM4 and multi-model mean methods. The downscaling method used in this study significantly improved NMME data reliability, although the degree of improvement may vary with time and location.

  PublisherDOI

• Impacts of change in multiple cropping index of rice on hydrological components and grain production in the Zishui River Basin, Southern China

  Agricultural Water Management · 2025-05-23 · 1 citations

  articleOpen access

  Recent declines in the rice Multiple Cropping Index (MCI) have reshaped grain production and water dynamics across Southern China, yet their effects on production stability and watershed hydrology, particularly in subtropical hilly regions, remain insufficiently studied. To address this, we extracted the current rice cropping structure in the Zishui River Basin (ZRB), Hunan Province, leveraging high-resolution Remote Sensing (RS) data. A planting suitability evaluation system for double cropping rice was developed by integrating climatic, soil, and site conditions through an Analytic Hierarchy Process (AHP) and GIS overlay. Based on these inputs, alternative rice cropping scenarios were simulated using the Soil and Water Assessment Tool (SWAT) to evaluate changes in hydrology and yield. The current rice planting scenario (S0) included 27.4 % single and 72.6 % double cropping areas, while 81.5 % of paddy fields were suitable for double cropping. The SWAT model, parameterized with RS-derived structures, achieved excellent streamflow simulation, with a Nash–Sutcliffe efficiency ( NSE ) of 0.86 and 0.88 during calibration and validation periods, and percent bias ( PBIAS ) of 4.5 % and 3.1 %, respectively. Simulation results indicated that the optimized rice planting structure (S3) enhanced rice yield with minimal hydrological impacts. Compared to S0, S3 increased irrigation, evapotranspiration, percolation, and rice yield by 4.8 %, 1.4 %, 5.5 %, and 4.0 %, respectively, while full double cropping scenario (S2) achieved an 11.0 % yield increase but raised irrigation demand by 11.2 %. The opposite results were found for full single cropping rice scenario (S1). This study demonstrates RS-coupled watershed modeling as a robust framework for optimizing rice cropping systems and promoting sustainable agriculture in subtropical hilly regions. • Integrated remote sensing and SWAT model enhancing agro-hydrological assessments. • Suitability evaluation identified optimal proportion and position for double rice. • Optimized multiple cropping index increased yield with small hydrologic impacts. • Irrigation and yield increased 12 % and 11 % under 100 % double cropping rice scenario.

  PublisherDOI

• Benefits and challenges of pasture cropping: Perceptions of grazing producers in the United States

  Rangelands · 2025-12-23

  articleOpen access

  • Pasture cropping has gained traction in Australia, but it remains a relatively novel idea in the United States. • Farmers who rely on grassland as their primary source of income are more likely to try pasture-cropping practices. • Pasture cropping could provide several key benefits, including improved forage quality, reduced input costs, improved animal health, and enhanced soil health and biodiversity. • The success of pasture cropping is influenced by several factors, including insufficient precipitation, which could pose a substantial challenge for producers in certain regions. • Farmers who received >170 mm of precipitation during the grass dormant season had the highest average success rate of 82% when pasture cropping.

  PublisherDOI

• List of contributors

  Elsevier eBooks · 2025-01-01

  book-chapter1st authorCorresponding
  PublisherDOI

• Deriving hydrological inferences from a machine learning model to understand the physical drivers of flow duration curves

  Journal of Hydrology · 2025-11-30

  articleOpen access

  • An explainable ML approach to identify key drivers of FDCs is presented. • The RF model adequately predicts FDCs in ungaged watersheds. • SHAP revealed global, regional and local controls on FDCs • Aridity Index and annual precipitation drive the scale of FDCs. • Baseflow Index, Seasonality Index, and geologic attributes control FDC shape. Flow Duration Curves (FDCs) provide a statistical relationship between the magnitude and frequency of streamflow in a watershed. The shape and scale of an FDC are dependent upon the dynamic relationship between streamflow regimes and the climatic, geological, topographical, and other environmental or anthropogenic attributes of the watershed. While Machine Learning (ML) models can provide enhanced predictive performance in ungaged watersheds over conventional approaches, validating the models’ comprehension of the underlying hydrologic processes is imperative for any stakeholder involvement. In this study, we employed Random Forest (RF) regression on individual Exceedance Percentiles (EPs) and slope of the FDCs for a large sample of watersheds in the contiguous United States. Then, we explored the interactions between watershed attributes and FDCs using SHapley Additive exPlanations (SHAP) to divulge the local (watershed scale), regional, and global (model scale) influence of attributes and their dependence structures. Results indicate that climate attributes (precipitation and aridity index) were the preeminent drivers in predicting FDCs across all quantiles, primarily affecting the scale of FDCs, followed by the baseflow index and geologic attributes that highly influence the low flow regime and control the shape of FDCs. The dominant controls of other watershed attributes, including precipitation seasonality, % snow, and elevation, vary between EPs and regions that were readily discernible in their SHAP values.

  PublisherDOI

• Reducing nitrate leaching and runoff through crop rotations in the Upper Mississippi River Basin

  Agricultural Water Management · 2025-09-17 · 1 citations

  articleOpen access

  Nitrate nitrogen (NO3-N) loss is the main source of water quality pollution in many agricultural regions. Mitigating NO3-N loss from croplands in a tractable manner has become a vital challenge. This study selected a heavy NO3-N loading area of the Upper Mississippi River Basin (UMRB) and applied a calibrated Soil and Water Assessment Tool (SWAT) model. The model was used to evaluate lateral and vertical NO3-N losses before and after crop rotation adjustments. Results showed that the risk of high NO3-N losses was greater under continuous corn than that of corn-soybean rotation in the baseline land uses, and NO3-N leaching was a more severe loss pathway than runoff. Moreover, the four crop rotation adjustment scenarios were effective in reducing NO3-N losses for the UMRB, especially for the lower reaches zones. The appropriate crop rotation patterns were then defined as meeting the 20 % reduction target threshold with the least deviation from baseline land uses. It was found that the appropriate patterns could reduce NO3-N leaching over 20 % compared to the baseline land uses, and also simultaneously reduced NO3-N runoff by more than 25 %, which further supported the appropriateness of selected crop rotation patterns. The final adjustments were mainly concentrated in the headwaters and lower reaches zones of the basin, primarily for continuous corn land use which had more severe risks. Overall, this study provided insights for mitigating NO3-N losses through runoff and leaching and highlighted the importance of integrated basin-wide management from multiple perspectives.

  PublisherDOI

• Evaluating Growth-Stage-Based Variable Deficit Irrigation Strategies for Improving Yield and Irrigation Water Use Efficiency of Grain Sorghum

  Journal of Natural Resources and Agricultural Ecosystems · 2025-01-01

  article

  Highlights Four irrigation levels were simulated over four sorghum growth stages, resulting in 256 scenarios. Boot to early grain filling stage was found to be the most sensitive stage to water stress. Maximum irrigation water use efficiency (IWUE) achieved with less irrigation than that required to achieve maximum yield. A strategy with 70% deficit in the first stage and 10% deficit in the remaining stages was found ideal based on yield in normal years. A strategy with 70% deficit in the first and fourth stages and 10% deficit in the remaining stages was found ideal based on IWUE in normal years. ABSTRACT. Recurring droughts, declining groundwater levels in the Southern Ogallala Aquifer, and projected warmer and drier future climate pose major challenges for irrigated grain sorghum [Sorghum bicolor (L.) Moench] production in the Texas High Plains (THP) region. Managed deficit irrigation during different growth stages could improve sorghum yield and irrigation water use efficiency (IWUE). This study aimed to develop and evaluate growth-stage-based variable deficit irrigation (GSVDI) strategies for grain sorghum production in the THP region under different weather conditions (e.g., dry, normal, and wet years). The CERES-sorghum module within the Decision Support System for Agrotechnology Transfer (DSSAT) Cropping System Model that was previously evaluated based on a grain sorghum-cotton rotation experiment at Halfway, TX, was used. Four growth stages of sorghum were considered, namely, emergence to panicle initiation (GS1), panicle initiation to boot (GS2), boot to early grain filling (GS3), and early to late grain filling (GS4). Long-term (1978-2019) simulations were conducted with combinations of four deficit irrigation levels (30%, 50%, 70%, and 90% evapotranspiration [ET] replacements) applied during the aforementioned growth stages, which resulted in 256 GSVDI scenarios. Efficient irrigation scenarios were identified as ones that resulted in both simulated yield and IWUE levels in the 95 th percentile of the 256 considered. Among them, the ideal GSVDI scenarios were identified for each weather category based on simulated sorghum yield and IWUE. The identified ideal GSVDI strategies were then compared with the control scenario (100% ET replacement in all growth stages). The results indicated that implementing GSVDI strategies could lead to significant irrigation water savings while maintaining higher sorghum yields. For example, S-64 (strategy of 30% ET replacement in GS1 and 90% ET replacement in GS2 to GS4) was identified as the ideal GSVDI scenario for normal years, and it saves 12.2% of irrigation water with a minor loss (1% less) in grain sorghum yield as compared to the control scenario. Suggested GSVDI strategies from this study could be very useful to optimize sorghum production while reducing groundwater withdrawals from the Ogallala Aquifer. Keywords: CERES-sorghum, DSSAT, Evapotranspiration, Ogallala Aquifer, Sorghum grain yield, Texas High Plains.

  PublisherDOI

Frequent coauthors

• Nithya Rajan

  22 shared

• Kritika Kothari

  University of Kentucky

  21 shared

• Sushil Kumar Himanshu

  20 shared

• James P. Bordovsky

  Texas A&M University System

  20 shared

• Steven A. Mauget

  Agricultural Research Service

  19 shared

• Paul B. DeLaune

  18 shared

• C. L. Munster

  Texas A&M University

  18 shared

• Curtis B. Adams

  Agricultural Research Service

  16 shared

Labs

• AgrohydrologyPI

Education

• B.S., Agricultural Engineering

  Andhra Pradesh Agricultural University

  1989

• M.S., Agricultural Engineering

  G.B. Pant University of Agriculture and Technology

  1992

• Ph.D., Agricultural & Biological Engineering

  Purdue University

  2009