Developing Organic Grain and Soyben Cropping Systems in Texas

Texas ranks first in number of farms and third in total agricultural production in the U.S. However, the state lags behind in organic crop production. The low rate of adoption of organic farming for grain crops such as corn and sorghum has been a major bottleneck for expansion of the organic livestock sector in this state. The specific goals of this study are based on our interactions with farmers and other stakeholders in the region. Collectively, these discussions have shaped the goals and objectives of the project. Our goals are: (1) Conduct research and on-farm demonstrations in a partnership between scientists and stakeholders for greater understanding of the influence of tillage and cover cropping on greenhouse gas emissions, weed dynamics, water-yield relations, and soil health, (2) Develop best management practices that optimize both agricultural profitability and ecosystem services in transitioning cropping systems (corn, grain sorghum, and soybeans), and (3) Develop an educational and outreach program for efficient transfer of project results to the various stakeholders and organize training efforts on the certification process, farm plan development, environmental benefits, and best management strategies while transitioning to organic production. Through this project, we will address some of the critical needs of farmers and other stakeholders in Texas who plan to adopt organic farming. Collaborators: Dr. Muthu Bagavathiannan and Dr. Ronnie Schnell (TAMU); Dr. S. Nair and D. Constance (Sam Houston State University)

Funded by USDA NIFA (2016-2019)
Graduate stduent working in the project: Diana Zapata
AgriLife Today Article

Unmanned Aerial Vehicles for Agricultural Remote Sensing

Unmanned aerial vehicles (UAV) are fast becoming the next generation tools for remote sensing applications in agriculture. The low operational cost, high temporal and spatial resolutions, easy-to-use controlling systems and high flexibility in image acquisition and planning make UAVs very popular compared to other remote sensing systems. We are conducting several studies in collaboration with a team of scientists at Texas A&M AgriLife Research invetsigating various applications of UAV remote sensing in cropping systems and bioenergy research. Sensor systems include multispectral, hyperpsectral and thermal sensors. Collaborators: Dr. Dale Cope. Dr. Muthu Bagavathiannan, Dr. Micheal Bishop, Dr. Ronnie Schnell, and Dr. Russ Jessup (TAMU); Dr. Juan Landivar (Texas AgriLife Research - Corpus Christi).

Review article published in Plos One
ASA Presentation; Poster Presentation

Funded by Texas A&M AgriLife Research (2016-2017)
Post-doctoral Research Associate working in the project: Sanaz Shafian

Cotton Phenotyping

Plant phenotyping is the quantitative determination of plant traits. Effective plant phenotyping methods should be able to identify plant growth characteristics with accuracy and precision. These techniques are usually noninvasive and allow researchers to analyze the quantitative and qualitative traits associated with interaction of genotypes with the environment. The overarching goal of this collaborative project is the development of standardized phenotyping procedures to identify high yielding and drought tolerant cotton cultivars in Texas using field-based and unmanned aeral vehicle-based methods. Collaborators: Dr. Geln Ritchie (Texas Tech University and Dr. Carlos Fernandez (Texas A&M AgriLife Research-Corpus Christi).

TPPA Presentation

Funded by Cotton Incorporated (2016-)
Graduate stduent working in the project: Miles Mikeska

Functioning of Agroecosystems: landscape scale processes & modEling

This is a multi-disciplinary and multi-institutional research and teaching project involving two agroecological regions in the southern U.S. Our approach will involve the collection and integration of soil, crop, and environmental measurements along with field-scale tower-based eddy covariance flux measurements. We will employ an integrated approach involving remote sensing, process-based simulation modeling, and advanced computational approaches such as machine learning to extend the field-based measurements to the full regional scales. This integrated project will provide more insights into the understanding of function of agroecosystems and the interplay among different agroecological variables. Combining existing modeling approached with novel model construction will provide direct comparison between traditional regression-based model and advanced kernel-based machine learning model.

Funded by USDA NIFA (2016-2018); Lead PI: Dr. Song Cui, Middle Tennessee State University
Graduate stduent working in the project: Dorothy Menefee


This project addresses one of the key challenges facing the biofuels industry: how to better manage bioenergy supply chains to meet future mandated production goals without negatively impacting water resources. The long-term goal of the project is to provide stakeholders with information and decision support tools to assist in identify opportunities for sustainable intensification of biofuel production without significantly impacting water resources.  Specifically, the project will (1) characterize water requirements and water productivities of diverse high-yielding, second generation lignocellulosic feedstocks of newly-developed feedstock crops in production regions with varying water resources, (2) identify feedstock traits associated with increased abiotic (especially water deficit) stress tolerance, and develop best management practices and technologies to maximize water use efficiency and minimize impacts on water resources during production and processing, (3) optimize and validate simulation models (using agronomic data from objectives 1 and 2) to identify combinations of feedstocks that match available water resources in target production regions, and (4) conduct life cycle analysis (LCA) and economic risk assessments associated with biofuels production under various feedstock production and water resource scenarios.

Funded by USDA NIFA (2016-2018)
Lead PI: Dr. John Jifon, Texas A&M AgriLife Research-Weslaco
Graduate stduent working in the project: Pramod Pokhrel