Recipient Organization
TEXAS A&M UNIVERSITY
750 AGRONOMY RD STE 2701
COLLEGE STATION,TX 77843-0001
Performing Department
(N/A)
Non Technical Summary
The sustainability of US cotton production is pivotal to agricultural sustainability in the Southern US. Protecting and improving soil health, reducing greenhouse gas (GHG) emissions, and enhancing input use efficiency are central to improving the sustainability of cotton in the face of changing climatic conditions. According to the most recent USDA-ERS report on tillage and conservation cropping in the US (Claassen et al. 2018), 60% of cotton acreage is still under conventional tillage, compared for example, to only 30% or less in soybean. Tillage intensity has increased due to the need to control herbicide-resistant and other problematic weeds (CAST 2012), which is now widespread throughout the US Cotton Belt (Heap 2023). Intensive tillage contributes greatly to greenhouse gas (GHG) emissions, stored carbon loss, and a loss of overall soil health, while conservation tillage is considered a vital component of a sustainable soil health management system (Elder and Lal 2008; USDA-NRCS 2017). Cotton is inherently a low residue crop, which in turn adds limited amounts of carbon during the growing season. Because cotton is commonly grown with low residue crops in rotation (Claassen et al. 2018), this is a major area where rapid gains can be achieved in soil health improvement and climate change mitigation. In this regard, cover crops and living mulches can play a key role (Wick et al. 2017). A recent analysis by the Cotton 2040 initiative revealed that cotton is a vulnerable crop to climate change impacts (WTW 2022). There is a crucial need to make cotton cultivation a carbon-neutral, or better yet a net carbon-positive enterprise, while simultaneously mitigating/adapting to the adverse effects of climate change. The majority of US cotton is already grown in marginal soils with low organic matter content. Potential increases in tillage to manage the anticipated surge in herbicide-resistant weeds associated with climate change (Ramesh et al. 2017) is expected to exacerbate this problem. Further, the use of Artificial Intelligence/Machine learning (AI/ML)-assisted precision application technologies can greatly improve resource input use efficiency, which can in turn reduce GHG emissions (e.g. Cotton Inc. 2018). These precision regenerative practices are also expected to create new market opportunities, through, for example, carbon crediting and sustainable cotton markets, and empower the rural agricultural workforce via employment generation.The long-term goal of the proposed research is to apply improved precision management practices to enable enhanced carbon sequestration and reduced greenhouse gas emission, pest control, and nutrient and water management; and to address labor challenges, steward land, create new market opportunities, and ensure a sustainable supply of climate-smart cotton. Achieving this will require a coordinated effort among researchers, Extension specialists, and broader industry stakeholders. The specific objectives of this project are: 1. Develop regenerative cotton production practices and investigate their long-term effects on US Belt-wide cotton production systems. 1.1. Establish soil organic carbon and carbon intensity baselines for ecoregions in the US Cotton Belt; 1.2. Investigate regenerative practices for reducing tillage and improving soil health; 1.3. Investigate the long-term impact of regenerative practices in addressing climate change using simulation models. 2. Develop and/or evaluate precision AI/ML and smart technologies for resource conservation and climate change mitigation/adaptation in cotton. 3. Evaluate the economic feasibility of precision regenerative production practices and determine new market opportunities. 4. Expand knowledge about farmers' multi-dimensional experiences of adoption of regenerative practices across the Cotton Belt. 5. Promote the adoption of regenerative, climate-smart cotton production practices through innovative and collaborative extension and outreach activities. 6. Provide educational opportunities to train the next generation of research and extension scientists and practitioners, including underserved communities, and empower the rural workforce.This integrated project is expected to bring tremendous benefits to the US cotton industry through the development and implementation of sound regenerative practices for reducing tillage and addressing climate-change impacts. By utilizing precision agriculture technologies, there will be an improvement in the timely diagnosis of field issues and implementation of site-specific measures for increasing input use efficiency and reducing GHG emissions. New market opportunities will be generated, improving overall farm profitability. The economic analysis will highlight the economic benefits of the adoption of precision regenerative practices and smart-technologies for cotton production. The health and safety of farm workers will be improved. Automation will help alleviate labor shortage in rural areas, while equipping them for new employment opportunities.
Animal Health Component
60%
Research Effort Categories
Basic
30%
Applied
60%
Developmental
10%
Goals / Objectives
The long-term goal of the proposed research is to apply improved precision management practices to enable enhanced carbon sequestration and reduced greenhouse gas emission,pest control, and nutrient and water management; and to address labor challenges, steward land, create new market opportunities, and ensure a sustainable supply of climate-smart cotton.The specific objectives of this project are:1. Develop regenerative cotton production practices and investigate their long-term effects on US Belt-wide cotton production systems1.1. Establish soil organic carbon and carbon intensity baselines for ecoregions in the US Cotton Belt 1.2. Investigate regenerative practices for reducing tillage and improving soil health1.3. Investigate the long-term impact of regenerative practices in addressing climate change using simulation models2. Develop and/or evaluate precision AI/ML and smart technologies for resource conservation and climate change mitigation/adaptation in cotton3. Evaluate the economic feasibility of precision regenerative production practices and determine new market opportunities4. Expand knowledge about farmers' multi-dimensional experiences of adoption of regenerative practices across the Cotton Belt5. Promote the adoption of regenerative, climate-smart cotton production practices through innovative and collaborative extension and outreach activities6. Provide educational opportunities to train the next generation of research and extension scientists and practitioners, including underserved communities, and empower the rural workforce
Project Methods
Objective 1. Develop regenerative cotton production practices and investigate their long-term effects on US Belt-wide cotton production systems. Objective 1.1. Establish soil organic carbon and carbon intensity baselines for ecoregions in the US Cotton Belt [Years 1 to 2]: To establish baselines, we will recruit 20-30 conventional cotton producers from each ecoregion where practices are considered 'business as usual'. In this project, a 'baseline scenario' or 'business as usual' site will be a cotton field where management for the past 5 years is considered typical for the region with minimal or no use of conservation management practices. Objective 1. 2. Investigate regenerative practices for reducing tillage and improving soil health. 1.2.1. Evaluate locally suitable regenerative production practices focusing on cover crops and living mulches in combination with conservation tillage on weed suppression, soil & water conservation, nutrient cycling, and pest dynamics [Years 1 to 4]: This objective includes four common experiments (CE 1 to CE4) to be conducted across multiple ecoregions to generate necessary research knowledge for promoting the adoption of regenerative practices. The CE1 will evaluate tillage, cover crop, and living mulch systems in controlled on-station field experiments across six ecoregions. The CE2 (3 sites) will evaluate cover crop planting method for cotton grown on beds in high-rainfall areas. The CE3 (4 sites) will focus on determining locally-suitable living mulch species for cotton across the six sites, whereas the CE4 (4 sites) will evaluate living mulch sensitivity to cotton herbicides for sound integration into existing weed management programs. 1.2.2. Track outcomes of reduced tillage and other regenerative production practices that create quantifiable changes in soil health, soil carbon, and GHG emissions [Years 1 to 5]: Soil samples (from Obj 1.1 and CE1) will be analyzed for various soil health indicators: soil pH (Thomas 1996), soil EC (Rhoades 1996), cation exchange capacity (Sikora and Moore 2014), extractable phosphorus (Olsen and Sommers 1982; Sikora and Moore 2014), phosphatase enzyme activity (Tabatabai 1994), extractable potassium, total nitrogen (dry combustion using an Elementar vario MAX Cube), wet aggregate stability (Fajardo et al. 2016), dry bulk density, soil organic carbon (dry combustion after pretreatment to remove inorganic carbon using an Elementar vario MAX Cube), soil respiration (laboratory incubation), and microbiome composition. 1.2.3. Investigate the impact of reduced tillage and regenerative production practices on soil microbiome and soil-borne pathogens in cotton [Years 1 to 4]: Alpha diversity will be obtained using amplicon sequence variant (ASV) method on QIIME2, and taxonomy curated with RDP and SILVA. Beta diversity will be assessed using Bray-Curtis and UNIFRAC distances, Co-occurrence networks will be built to identify keystone bacterial taxa in different samples (Berry and Widder 2014). 1.2.4. Evaluate circular buffer strips of native perennial grasses in center pivot irrigated cotton fields to improve soil health and ecosystem services [Years 1 to 4]: We are proposing a large landscape-level trial at Clovis, NM. A perennial grass mixture of two cool-season grasses and five warm-season grasses was planted in buffer strips in 2016 summer as part of a corn study that is still in place. 1.2.5. Evaluate and determine root traits of cotton for sustainability under climate change [Years 1 to 4]: Screen for cotton cultivars with root characteristics that can increase nutrient (N, P) and water uptake, C sequestration, and weed suppression. Images will be taken and analyzed for root angles, numbers, complexity, and density using the Rhizovision software (Seethepalli et al. 2020; Singh et al. 2021). Objective 1.3. Investigate the long-term impact of regenerative practices in addressing climate change using simulation models [Years 1 to 4]: We will use the widely used process-based cotton simulation modeling platform CSM-CROPGRO-Cotton model in DSSAT.Objective 2. Develop and/or evaluate precision AI/ML and smart technologies for resource conservation and climate change adaptation/mitigation in cotton. 2.1. Investigate crop water use requirements and update ET-based irrigation scheduling recommendations in cotton under cover crop and living mulch systems; 2.2. Develop sensor-based smart irrigation systems integrated with ET measurements and soil variability maps for efficient irrigation scheduling [Years 1 to 4]: In this objective, we will use two measurement techniques for quantifying ETc. This system will be combined with a sensor-based (e.g. Vories and Sudduth 2021; Dominguez-nino et al. 2020) as well as a soil-texture variability map (Vories et al. 2020) to improve the accuracy of irrigation scheduling. 2.3. Improve the diagnostics of nutrient deficiency symptoms in cotton for site-specific nutrient management; 2.4. Develop unmanned aerial system (UAS)-based diagnosis and site-specific treatment framework for major pest organisms (weeds, insect pests, diseases) in cotton [Years: 1 to 4]: Traditionally, the Normalized Difference Vegetation Index (NDVI) has been used for variable rate nitrogen applications, using sensors mounted on tractors. In this regard, drone-based sensors may provide a more robust determination of nitrogen deficiency at spatial scales using multiple plant features (with the use of AI/ML), which can be integrated with existing variable rate applicators.Objective 3. Evaluate the economic feasibility of precision regenerative production practices and determine new market opportunities [Years 1 to 5]: Co-PD Hudson will analyze economic feasibility in three primary ways. First, partial budget analysis will be used to determine changes in costs of production. Second, PD Hudson will work with the modeling team to produce model estimates of yield outcomes under alternative practices through Monte Carlo simulations. Finally, PD Hudson will examine the impacts of alternative policies such as carbon markets and "sustainable" cotton product marketing.Objective 4. Expand knowledge about farmers' multi-dimensional experiences of adoption of regenerative practices across the US Cotton Belt. [Years 1 to 3]: Human dimensions research will be conducted by the University of Georgia (co-PI Thompson) using established and innovative methodologies. While traditional adoption frameworks treat adoption as binary, a new framework developed by Han and Niles (2023) proposes four key dimensions of adoption, each of which can be implemented to varying degrees (i.e., simple to complex, or partial to full): entirety, variability, longevity, and sophistication.Objective 5. Promote the adoption of regenerative, climate-smart cotton production practices through innovative and collaborative extension and outreach activities [Years 2 to 5]: We will specifically develop outreach programs with the following targets: 1) Improve awareness about cotton sustainability metrics, 2) Engage producers to explore incentives through adoption of regenerative practices, and 3) Demonstrate project findings. AgriCenter International, for example, attracts 1.3 million visitors annually. We will work with Field to Market to utilize their free Fieldprint Calculator.Objective 6. Provide educational opportunities to train the next generation of research and extension scientists and practitioners, including underserved communities, and empower the rural workforce [Years 1 to 5]: Our educational program will focus on 1) Establishing innovative, multi-agency training; 2) Enhancing educational programs for improving the readiness of graduates; and 3) Development of a Youth program. All PDs will be recruiting undergraduate research students to assist with their field and lab research activities, and we will give preference to underrepresented and low-income students during recruitment.