Monitoring Cotton Growth in the Texas Panhandle Using Computer Simulations

Date

2024-03-07

Authors

Marcillo, Guillermo

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Abstract

Cotton is a profitable crop with a low water consumption footprint compared to other row crops, which makes it suitable for water conservation strategies in the TX Panhandle. However drastic variation of climatic patterns coupled with future economic uncertainty would severely impact cotton decision making. Furthermore, insufficient field data as well as a lack of tools adapted to the unique conditions of the region limit the options for growers to make efficient use of inputs and maximize their yields. In this project we propose the initiation of a digital system for monitoring cotton crop conditioning in West Texas. Such a system will merge large volumes of information resulting from computer assisted simulations, remote sensing imagery, and field experiments so users can accurately keep track of cotton phenology - a fundamental indicator of crop health to inform field decisions.

Description

Cotton has gained relevance in the Texas Panhandle despite its recent introduction in the region. Further, cotton production is part of regional conservation efforts to adapt to declining water levels in the Ogallala Aquifer. Nevertheless, growers face increasing weather and market uncertainty as they prepare strategically for new growing seasons every year. Insufficient field data as well as a lack of tools adapted to the unique conditions of the region limit the options for growers to make efficient use of inputs and maximize their yields. At present, there are neither specialized cotton databases nor decision support systems based on simple indicators of crop performance that support decision-making in the North Texas High Plains. Preliminary data has shown that early Phenology observations from cotton trials conducted at two locations (Bushland and Etter, TX) were used for model calibration. Weather and soil data for these locations (Air Temperature, Precipitation, Solar Radiation, Soil Texture, Soil Hydraulic Conductivity) were sourced from public spatially gridded-databases (Daymet-3, SSurgo). Also, a curated repository of sixty 1-km2 pre-clipped and filtered images at 0.5 x 0.5 m-resolution were obtained from a private provider (e.g., SKYWATCH8), consisting of rasterized images and multiband spectral signatures. The monitoring system currently involves a data flow network coded in Python and R which extracts historical weather datasets (1980-2022) and profiles soil data for a field at 4 depth layers (total depth: 0-120 cm). Weather and soil files are then passed as inputs for running a field-simulation using the Agricultural Production SIMulation system, APSIM). Additional scripts in Python and the R-language for visualization of model outputs, parameter optimization, and scenario-analyses are currently maintained in a private GitHub repository.

Keywords

2024 Faculty and Student Research Poster Session and Research Fair, West Texas A&M University, College of Agriculture and Natural Science, Poster, Water conservation, Cotton

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