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Why the eddy covariance technique is an ally in the search of sustainable agriculture

Why the eddy covariance technique is an ally in the search of sustainable agriculture

By Agricompas Crop Model Team

In recent decades, agriculture has been under the scrutiny of society and the scientific community due to the negative impact that it generates on the environment. These impacts are of many types, including deforestation, eutrophication of water bodies, the reduction of biodiversity due to the intense use of pesticides, and the emission of greenhouse gases (GHG).

In relation to GHG emissions, these are released in the process of manufacturing inputs, such as, fertilizers. Also included are the GHGs released as a result of the transport process: first of inputs towards the production areas, and then of the product towards the consumption areas.

An eddy covariance system recording greenhouse gases emissions on a commercial rice field at Colombia for EcoProMIS project. (Agricompas)

 

 

A Complicated Task

But the most complicated task from a methodological point of view is to determine the GHGs that are released during the production stage. Among the GHGs released to the atmosphere during the production phase, the most important are carbon dioxide, methane (in systems where the soil is in anaerobic conditions), nitrous oxide, and ammonia.

Methodological difficulties are associated with the fact that these emissions are determined by dynamic factors such as climate, soil characteristics, and management practices, especially fertilization and irrigation.

Since it is impossible to survive without agriculture, efforts have focused on developing and implementing production systems able to maximize yields while reducing negative effects on the environment. A prerequisite for advancing in this direction is to measure the GHGs generated during agricultural production cycles.

Static Chambers

To understand better the methodological challenges involved in determining these gases under field conditions, let us take methane as an example. This gas is generated as a product of the decomposition of organic matter in the soil under non-oxygen conditions, typical of crops such as flooded rice.

Traditionally, static dark chambers have been used to collect samples that are later analysed by the gas chromatography technique in specialized laboratories.

This technique has a high sensitivity to determine low methane fluxes, is easy to handle, and has a low cost. But its main disadvantages are related to the low spatial representativeness and the inability to generate data at different time scales.

In other words, the measurements only represent the gas flux in a small area and at a specific time point, which leads to the question: can this technique generate data to represent what happens in inherently heterogeneous and dynamic agricultural systems?

Eddy Covariance

It is in this context that the technique of eddy covariance appears, as an alternative way to measure, among other variables, methane flows with greater spatial and temporal representativeness.

This technique employs a complex assembly of sensors arranged in a tower (which is why they are usually called eddy covariance towers) that records variables that ultimately allow the determination of the exchange of gases and energy between the crop and the atmosphere.

Although the foundations of the technique and data processing are complex, it provides useful information in the search for more sustainable agricultural systems.

This is because, in addition to determining GHG emissions, such as methane and carbon dioxide, the eddy covariance technique also provides information about the flow of energy between the soil, the plant, and the atmosphere. This means that information is also useful to improve the water use efficiency since the measurements allow the determination of water fluxes from the crops to the atmosphere (evapotranspiration).

All of this information is comparable in terms of accuracy with data obtained by reference instruments such as lysimeters. Therefore, the technique of eddy covariance is currently a powerful ally in the search for more sustainable agricultural systems.

Use with EcoProMIS

The EcoProMIS project has four eddy covariance towers in Colombia, two recording data on rice crops, and two on oil palm crops. The data collected by these stations are being processed to calibrate crop models that allow, in addition to predicting yields, to estimate GHG emissions.

Together with our partners (CIAT, Cenipalma, Fedearroz, IWCO, Pixalytics and Solidaridad), the final objective of the project is to generate “knowledge and decision support” to orient stakeholders towards sustainability.

EcoProMIS uses cutting-edge UAV technology

EcoProMIS uses cutting-edge UAV technology

Michael Gomez Selvaraj, Crop Physiologist at CIAT

Behind the farmer mobile apps, workshops and shiny interface of EcoProMIS, a creative and dedicated team are working to collect and process data. The team are developing our data platform, which is unique in the breadth of data that is collected, including crop information, greenhouse gas emissions, farmer interviews, and satellite and drone imagery.

Birds Eye View

Across our pilot sites in Colombia, our colleagues are recording all of this information. One of the most exciting parts of the job is the flying of drones above farmers’ fields to capture high-resolution images.

At EcoProMIS, our fleet of Unmanned Aerial Vehicles (UAVs, the technical term for drone) make regular flights to capture these images. They are not standard photos, but have a high spatial and high temporal resolution, captured by special cameras and sensors attached to each UAV.

Our fleet of drones includes octocopters and quadcopters, vehicles with eight and four rotating blades respectively. By using drones, we can collect data in a non-invasive way and with greater accuracy and cost-effectiveness than historical ‘boots on the ground’ data collection.

This is cutting edge technology and together with the other data inputs gives EcoProMIS unrivalled understanding of each farm.

 

CIAT team with one of the Unmanned Aerial Vehicles used for field observations

 

Image Analysis

The UAV images collected in each field are sent to the CIAT phenomics platform, a computer that can process and ‘stitch together’ all of the images. As the science partner on the EcoProMIS project, CIAT are based in Cali, Colombia, where their team of scientists merge the data from thousands of high-resolution images.

To merge and analyse these images, they use fully automated software. This is CIAT’s Pheno-i image analysis framework and provides the solution for processing the high volume of raw images.

From the result of the analysis, the team are able to extract vegetation indices, a form of information that can indicate plant health and productivity. Correlations can be made between vegetation indices and key crop agronomic traits, which provides the information required to build the crops models and then pass on the knowledge to support farmers.

The drone images are further enhanced by combining them with satellite images of the farms. This is another exciting part of our work, to be covered in more detail in a future blog article, and is delivered by the UK company Pixalytics. As a project funded by the UK Space Agency, the use of satellite data was a key component of the project design and appeal.

The result of this UAV and satellite data is a highly advanced and accurate product to serve the agricultural sector.

Knowledge for Farmers

The use of the UAVs allows us to gather sophisticated data which can then provide support to farmers in the form of a knowledge-rich mobile application.

The EcoProMIS team is currently developing the first of these ‘knowledge services’ for growers of rice and oil palm. These services, built on the drone images and other data, will predict yield and provide decision support to the growers.

Yield prediction is one of the most valuable pieces of knowledge for a farmer. With this knowledge at their fingertips, the grower can understand if the crop is performing well and if this is not the case to investigate and address limiting growth factors. Furthermore, as growers continue to interact with EcoProMIS, using the knowledge and uploading their own data, the crop model will improve in accuracy.

It is our intention that the drone data, combined with the other sources of farmer data, will provide a strong ally to growers for the shared ambitions of achieving food security and environmental sustainability.