Precision Agriculture: The Role of Science Presented by Dr. Eduardo Segarra Department of Agricultural and Applied Economics, Texas Tech University

Purpose of presentation Highlight the relevancy of “science” based research on agriculture, and highlight the importance of hedonic pricing

The Agricultural Sector in the 21st. century will be called upon to provide an abundant, diverse, safe, and of high quality supply of food and fiber at reasonable prices for consumers…. and which is globally competitive, profitable for producers and processors, and minimizes environmental degradation

Traditional agricultural crop production practices have been based on broad input utilization prescriptions that ignore site- specific characteristics of crop fields within farms and/or across regions

Precision agriculture, precision farming, site-specific management, or also referred to as remote- sensing farming internalizes unique features of crop fields to tailor precise input utilization

Specifically, What is Precision Agriculture? Precision agriculture deals with within-crop field disaggregation of inherent and applied factors of production or other characteristics which have significant impacts on the overall productivity (amount and quality of output produced) and environmental implications of crop production

SPATIAL issues addressed by Precision Agriculture practices Soil fertility Soil water holding capacity Weed and pest infestations Fertilizer use Irrigation water use Chemical use (herbicides & insecticides) Quality of output produced Yield potential

Spatial Variability of Soil Properties l Organic Matter l pH l Nitrogen l Phosphorus l Depth to caliche Slope & altitude l Hydraulic properties

Nitrogen Lbs/A

Nitrogen

Greenbug Damage

Yield

NO3-N Pre-Season Residual Map from 0 to 12 Inches of Soil Depth, Gaines County, Texas. Peanut. NO3-N Pre-Season Residual Map from 0 to 12 Inches of Soil Depth, Gaines County, Texas.

Peanut. Optimal Levels of Spatial Nitrogen Application Map for Precision Farming Practices, Gaines County, Texas.

Spatial Peanut Yield Map for Precision Farming Practices, Gaines County, Texas.

Spatial Peanut Yield Map for Whole-Field Farming Practices, Gaines County, Texas.

Peanut. Spatial Net Revenue Above Nitrogen and Water Costs for Precision Farming Practices, Gaines County, Texas.

Peanut. Spatial Net Revenue Above Nitrogen and Water Costs for Whole-Field Farming Practices, Gaines County, Texas.

Probability Density Function for Peanut Net Revenues Above Nitrogen and Water Costs.

Cumulative Density Function for Peanut Net Revenues Above Nitrogen and Water Costs.

Corn. NO3-N Pre-Season Residual Map from 0 to 24 Inches of Soil Depth, Halfway, Texas.

Corn. Optimal Levels of Spatial Nitrogen Application Map for Precision Farming Practices on a Per-Year Basis for a Ten-Year Planning Horizon, Halfway, Texas.

Spatial Corn Yield Map for Precision Farming Practices, Halfway, Texas.

Spatial Corn Yield Map for Whole-Field Farming Practices, Halfway, Texas.

Corn. Spatial Net Revenue Above Nitrogen and Water Costs for a Ten-Year Optimization Model for Precision Farming Practices, Halfway, Texas.

Corn. Spatial Net Revenue Above Nitrogen and Water Costs for a Ten-Year Optimization Model for Whole-Field Farming Practices, Halfway, Texas.

Probability Density Function for Corn Net Revenues Above Nitrogen and Water Costs.

Cumulative Density Function for Corn Net Revenues Above Nitrogen and Water Costs.

Cotton. NO3-N Pre-Season Residual Map from 0 to 12 Inches of Soil Depth, Lamesa, Texas.

Cotton. Optimal Levels of Spatial Nitrogen Application Map for Precision Farming Practices on a Per-Year Basis for a Ten-Year Planning Horizon, Lamesa, Texas.

Cotton. Spatial Net Revenue Above Nitrogen and Water Costs for a Ten-Year Optimization Model for Precision Farming Practices, Lamesa, Texas.

Cotton. Spatial Net Revenue Above Nitrogen and Water Costs for a Ten-Year Optimization Model for Whole-Field Farming Practices, Lamesa, Texas.

Probability Density Function for Cotton Net Revenues Above Nitrogen and Water Costs, Lamesa, Texas.

Cumulative Density Function for Cotton Net Revenues Above Nitrogen and Water Costs, Lamesa, Texas.

Derivation of PA Practices Determine plant growth conditions on a per unit land area basis Understand the interactions of plant stress and applied inputs on output production AND quality Use variable rate technology to apply inputs where AND when needed Develop decision aids for improved crop management (yield and quality)

TECHNOLOGY TRANSFER Irrigation and Fertilizer use - according to Best Management Practices (BMP) IPM on a per unit land area basis Optimization of profits (amount of output produced and its quality) Minimization of environmental damage