Skip to main content
ARS Home » Plains Area » Lubbock, Texas » Cropping Systems Research Laboratory » Wind Erosion and Water Conservation Research » Research » Publications at this Location » Publication #231763

Title: Deficit irrigation for enhancing sustainable water use: Comparison of cotton nitrogen uptake and prediction of lint yield in a multivariate autoregressive state-space model

item LI, HONG - Texas Agrilife Research
item Lascano, Robert

Submitted to: Soil Use and Management
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/4/2010
Publication Date: 6/1/2011
Citation: Li, H., Lascano, R.J. 2011. Deficit irrigation for enhancing sustainable water use: Comparison of cotton nitrogen uptake and prediction of lint yield in a multivariate autoregressive state-space model. Soil Use and Management. 71(2):224-231.

Interpretive Summary: As the irrigation-water from the Ogallala aquifer diminishes the management of this limited resource to maximize crop yield is needed. However, this management is complicated because soils across the landscape are variable and well capacities range from 0.1 – 0.3 inch per day, which in many cases do not provide the daily water requirements that crops need. In addition, the amount of water a crop uses is directly related to the nitrogen (N) used by the crop, e.g., the higher the crop water the more N the crop will need. The practice of not supplying the crop with the daily requirement is known as deficit-irrigation and maximizing cotton lint yield in the High Plains of Texas under this scenario is needed. An experiment to answer these questions was conducted near Lamesa, TX under two deficit irrigation treatments. In these experiments, we measured N and water uptake, and canopy temperature and reflectance across two transects in a large field. All variables were measured weekly throughout the growing season and cotton lint yield were determined at the end of the season. A state-space model of four variables describes the variability of cotton lint yield across the landscape in the High Plains of Texas. These variables are a vegetative index that describes plant biomass, soil water content, soil nitrogen availability, and elevation, all as a function of position in the landscape. The advantage of this model is that it provides a management tool to maximize how nitrogen fertilizer and water are applied in a given field. For example, elevation dictates how water from rainfall is distributed across the landscape and that lower elevations will collect more water. Furthermore, lower elevations tend to have more clay and thus will hold more water when compared to higher elevations, which are usually eroded due to wind and have shallower soil depths. This model can be used as a guide to how to manage limited irrigation-water and N across the field to maximize lint yield.

Technical Abstract: Plant adaptation to a limited water supply may be associated with the soil's water holding capacity and landscape features that affect the hydrology. Knowing the response of a crop to water shortage and associated plant water stress symptoms is critical to manage deficit-irrigation, and minimize plant stress. A field-study of plant response to a limited water supply at a landscape-scale was conducted on the southern High Plains of Texas. The objectives were to quantify cotton (Gossypium hirsutum L.) lint yield in a limited-water environment by measuring whole plant N uptake, canopy infrared temperature, and plant multi-spectral reflectance, in relation to soil texture, and soil water and NO3-N availability across the landscape. The treatments were two deficit irrigation levels at 50% and 75% of cotton potential evapotranspiration (ET). All variables were measured weekly and 15-m apart along the center-pivot irrigation circles. Plant reflectance in the visible, near infrared (NIR), and mid infrared (MMIR) bans, canopy infrared temperature, and normalized difference vegetative index (NDVI) were correlated with elevation, soil water content (SWC), whole plant biomass, and total N uptake (-0.69