- Agronomic: sugarcane
- Education and Training: decision support system, farmer to farmer, on-farm/ranch research, participatory research
First introduced into Louisiana in 1751 sugarcane is the highest valued row crop in the state. Its continuous production is an important historic and economic component of Louisiana’s overall economy. While recent decades have seen a drop in Louisiana sugarcane acreage, crop values have remained stable due to increases in sucrose yield. Significant increase in yield is attributable to the addition of nitrogen fertilizer beyond any other agricultural output.
As gross tonnage of harvested sugarcane declines, sugarcane farmers in Louisiana are under pressure to boost operational efficiency in order to sustain this economically vital crop. It is essential that producers apply new and proven technology in order to mitigate the cost of production, maximize yield and limit impact on the environment.
Intense agricultural production systems, such as sugarcane, typically require higher nitrogen rates to achieve sufficient biomass and yield. Along with N rate optimization, N application timing is also an important consideration. Development of effective and affordable N management systems is essential to maintain sustainable sugarcane agriculture in Louisiana, both from an economic and environmental perspective.
Methods currently employed to determine crop N status in sugarcane include visual inspection, tissue analysis, and chlorophyll meters. Soil N analysis is also utilized to gauge N soil content, but the reliability of soil sampling techniques is inconclusive due to challenges associated with the nature of soil N, especially in the humid alluvial soils of southern Louisiana.
The Normative Difference Vegetation Index (NDVI) is a form of hyper-spectral imaging that collects and processes information across specific wavelengths of the electromagnetic spectrum. The purpose of NDVI image processing is to obtain a spectral index of each pixel in an image. In agriculture, the goal of NDVI imaging is to detect the relative strength of photosynthetic processes in each area of a field. Hyper-spectral methods such as NDVI have the main advantage that the entire spectrum may be acquired at every point without prior knowledge of the sample; post processing allows all information in the dataset to be mined in any number of ways after the fact. The primary disadvantage of traditional methods is their cost and complexity.
Studies on Louisiana sugarcane growth have shown that remote sensing methods like NDVI are effective in predicting sucrose yield in response to applied N fertilizer. Yet access to these technologies by farmers is limited; acquiring, processing and interpreting such data is costly and time-consuming. While high resolution aerial imagery such as NDVI holds the potential to improve operational efficiency, economic factors prevent their general adoption.
High temporal and spatial resolution NDVI can provide a host of potential uses in agriculture: prescribing N fertilizer amounts and estimating crop yield are two of the potential benefits of Louisiana sugarcane producers. Yet a chicken and egg problem exists in allocating time and resources necessary for technology that has yet to be proven in the field. Our study intends to make these tools available in order to refine and measure their effectiveness.
Project objectives from proposal:
To what degree do variable N rates applied to sugarcane correlate with low-cost NDVI measurements?
Is our in-season model predictive of sugarcane yield potential, based on time-series analysis of low-cost aerial NDVI?