Source: WEST TEXAS A&M UNIVERSITY submitted to
Sponsoring Institution
National Institute of Food and Agriculture
Project Status
Funding Source
Reporting Frequency
Accession No.
Grant No.
Project No.
Proposal No.
Multistate No.
Program Code
Project Start Date
Apr 1, 2023
Project End Date
Mar 31, 2027
Grant Year
Project Director
Ma, X.
Recipient Organization
Performing Department
Non Technical Summary
Increasing global demand for food has necessitated the growing external chemical input to sustain and improve agricultural productivity in the past century. However, this approach is not sustainable due to the environmental degradation caused by the everincreasing accumulation of agrichemicals in the environment. With the rapid development of nanotechnology in the past decades which has dramatically changed the landscape of modern science and technology, the application of nanotechnology, or the incorporation of nanoparticles into agrichemical formulahas become increasingly popular due to their high reactivity, slow releasing of critical nutrients which enhances their utilization rate and many other benefits. However, as any otheragrichemicals, broad applications of nanoagrichemicals will result in their gradual buildup in agricultural soils which may negatively affect soil quality and fertility. Unfortunately, the long-term impact of nanoagrichemicals on soil quality and crop yield is still poorly understood even though some short-term studies have been performed, which significantly hinders the potential beneficial applications of nanotechnology in agriculture. The primary goal of this project is to understand the long-term, multigenerational impact oftwo agriculturally relevant metal oxide nanoparticles: zinc oxide and copper oxide nanoparticles, and their transformed products on the health of rice paddy soils and rice yield. Rice is chosen as our model cropbecause it is a staple food crop globallyand a key source of export in several Southern states.Well controlled greenhouse studies will be performed over four generations of rice growing in paddy soils treated with different nanoparticles and their transformed products at different scenarios. At the end of each generation, soil and plant samples will be collected. Critical soil chemical and biological parameters that indicate soil health will be analyzed. For plant samples, the plant biomass, grain yield, grain quality as well as important nutrient and metal contents in rice grains will be measured. The results will shed light on the long-term impact of nanoagrichemicals on soil health and crop yield, a critical need before nanotechnology can be fully embraced in agriculture. In addition, machine learning will be employed to establish possible connections between the properties of nanoagrichemicals and their long-term impact on soil health and crop yield, generating important insight into the beneficial uses of nanoagrichemicals while avoiding their harmful effects.
Animal Health Component
Research Effort Categories

Knowledge Area (KA)Subject of Investigation (SOI)Field of Science (FOS)Percent
Knowledge Area
102 - Soil, Plant, Water, Nutrient Relationships;

Subject Of Investigation
0110 - Soil;

Field Of Science
2020 - Engineering; 2000 - Chemistry;
Goals / Objectives
With an increasing role of nanotechnology in advancing agricultural productivity and in society, it is imperative to understand the consequences of intentionally or incidentally introduced nanoparticles on soil health because previous sudies have shown that nanoparticles closely interact with many components of the soil matrix. In particular, the impact of prolonged exposure to nanoparticles over several generationson soil health and crop production must be known to ensure sustainable applications of nanotechnology in agriculture. The overaching goal of this project is to systematically assess the multigenerational impact of two engineered nanoparticles: zinc oxide and copper oxide nanoparticles, their transformed products and bulk and ionic forms on paddy soil health and rice production over four generations. Rice system is selected due to the unique biogeochemistry observed in paddy soil and the global significance of rice as a staple food. Specific objectives include: (1) determine the impact of chosen nanoparticles and their bulk and ionic counterparts on representative chemical and biological soil health parameters, (2) evaluate their effects on rice production and grain quality, (3) elucidate the multigenerational impact of these chemicals on soil health and rice production at different application scenarios, and (4) explore the essential connections of different soil parameters, soil health and crop yield through machine learning.
Project Methods
The project will adopt an integrated approach of experimetnal studies and machine learning. For the experimental studies, we will grow rice in paddy soils collected from atual paddy sites in Texas. The experiments will be performed in greenhouse to ensure that four generations of rice growth can be completed within the project period. At the end of each generation, bulk and rhizosphere soil and plant tissues, including rice grains will be collected. a range of parameters indicating soil health such as soil pH, soil organic carbon content, soil enzymatic activities, microbial community, and availability of toxic metalloids and rice yield such as the grain mass and grain quality will be experimentally determined. Nanoparticles and their transformed products will be carefully characterized. The results will be processed following standard protocols using statistical methods. In addition to the proposed experimental activities, machine learning aiming to establish correlations between different soil parameters, and between soil health, plant health and the grain yield of rice will be performed. For the machine learning studies, an raw dataset that includes both results from our own studies and results from the literature will be established. The raw data will undergo typical data standardization to eliminate outliners and the rest data will be used train and verify machine learning models.