Articles
INNOVATIVE USE OF SENSORS TO COLLECT, ANALYZE AND FORECAST ABIOTIC FACTORS IN ORDER TO IMPROVE PRODUCTIVITY
Article number
1054_41
Pages
341 – 350
Language
English
Abstract
The main goal of all agricultural systems is to improve production while at the same time reduce the cost of production.
This is essential with the exponential growth of the worlds population in order to achieve food security and sustainable agriculture.
Both of these are main goals of the European Union Horizon 2020 program.
There are a number of different ways to try to accomplish this.
One way is through the accurate forecasting of the factors that highly influence plant growth and production.
Such factors are typically climatic and edaphologic factors.
This is particularly true today because of the impending impacts of climate change.
This phenomenon leads to abrupt climatic and edaphologic changes which alter the normal function of the plants ecological cycles. Knowing and understanding present climatic and edaphologic conditions and how those might change in the future, allows for effective and efficient management of agricultural and other natural ecosystems.
In this paper, we present the conceptual model of how this is intended to be accomplished.
Specifically in this study, models are developed to help provide valuable information regarding the climatic and edaphologic changes for an agricultural and forested ecosystem, specifically a vineyard and a pine stand.
A system will be developed that will be composed of a main station and wireless sensors.
One station will be placed in each study site with a number of sensors that will be embedded with probes that will record the abiotic factors of interest on a continuous daily basis.
These measurements will be more frequent during the growing season.
To record all necessary abiotic factors, the sensors will be placed above and below the ground adjacent to the plants of interest.
The time-series data to be recorded will be used afterwards for the forecasting analysis that will be done based on non-linear physical models (specifically chaos theory) and neural networks.
These models will forecast the short-term climatic and edaphologic changes.
In addition, the major biotic factors for growth and production at the studied sites will be measured and combined with the abiotic factors.
This will help in the understanding of how the forecast climatic and edaphologic changes can affect the future productivity of the vineyards and the sustainability of the pine forests.
This will enable viticulturists and forest managers to be proactive and take the appropriate corrective management actions to improve production and/or quality.
This is essential with the exponential growth of the worlds population in order to achieve food security and sustainable agriculture.
Both of these are main goals of the European Union Horizon 2020 program.
There are a number of different ways to try to accomplish this.
One way is through the accurate forecasting of the factors that highly influence plant growth and production.
Such factors are typically climatic and edaphologic factors.
This is particularly true today because of the impending impacts of climate change.
This phenomenon leads to abrupt climatic and edaphologic changes which alter the normal function of the plants ecological cycles. Knowing and understanding present climatic and edaphologic conditions and how those might change in the future, allows for effective and efficient management of agricultural and other natural ecosystems.
In this paper, we present the conceptual model of how this is intended to be accomplished.
Specifically in this study, models are developed to help provide valuable information regarding the climatic and edaphologic changes for an agricultural and forested ecosystem, specifically a vineyard and a pine stand.
A system will be developed that will be composed of a main station and wireless sensors.
One station will be placed in each study site with a number of sensors that will be embedded with probes that will record the abiotic factors of interest on a continuous daily basis.
These measurements will be more frequent during the growing season.
To record all necessary abiotic factors, the sensors will be placed above and below the ground adjacent to the plants of interest.
The time-series data to be recorded will be used afterwards for the forecasting analysis that will be done based on non-linear physical models (specifically chaos theory) and neural networks.
These models will forecast the short-term climatic and edaphologic changes.
In addition, the major biotic factors for growth and production at the studied sites will be measured and combined with the abiotic factors.
This will help in the understanding of how the forecast climatic and edaphologic changes can affect the future productivity of the vineyards and the sustainability of the pine forests.
This will enable viticulturists and forest managers to be proactive and take the appropriate corrective management actions to improve production and/or quality.
Authors
V. Iakovoglou, G.N. Zaimes, D. Emmanouloudis, A. Ioannou, D. Bandekas , L. Magafas, C. Giordamlis , P. Kouris
Keywords
sustainable agriculture, vineyards, Greece, stochastic models, climate change
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