Impact of climate change on avocado phenology and native sclerophyllous forest according to access to potential groundwater in the Aconcagua River basin
Climate change is having two primary impacts in Chile. First, through the sustained reduction in precipitation, and second, through rising temperatures. This has led to a significant reduction in water resources, affecting both ecosystems and agricultural production, especially in the Aconcagua River basin.
Rising temperatures have an impact by increasing vegetation demand for water and by shifting vegetation phenological development stages. This threatens biodiversity and agricultural security, particularly in crops such as avocado, which require up to 18,000 m³/ha annually in certain areas.
To address this problem, a comprehensive system is proposed that combines groundwater zoning with a phenological analysis of avocado orchards and native forests. The objective is to determine the relationship between potential groundwater zones and the phenology of avocado trees and native sclerophyllous forests in the Aconcagua Basin, using zonation techniques and phenological analysis using remote sensing and artificial intelligence.
A multi-scale drought observatory for Chile: an early warning system to mitigate agricultural and ecological impacts
Since 2010, central Chile is experiencing a multi-dimensional crisis due to the pervasive impacts of the ongoing mega drought. We propose the development of a multi-scale drought observatory for Chile that will help mitigate agricultural and ecological impacts.
Using a macrosystems perspective, this observatory will integrate global climate and land satellite data with in-situ measurements from national weather stations. It will have two main components: i) monitoring climate and land variables and their impacts on agricultural and socio-ecological systems, and ii) a drought early warning system (DEWS).
The platforms will target three user groups – the general public, decision makers, and the scientific community - and will comply with FAIR data principles to enhance usability of all products.
SatOri: Satellite System for Irrigation Optimization
Water scarcity due to climate change has severely impacted agriculture in central Chile. This project developed SatOri, a low-cost, satellite-based decision-support service to optimize irrigation in kiwi and cherry orchards.
The innovation integrates two key components: (1) determination of optimal deficit irrigation strategies (RDC) that reduce water use without compromising yield or fruit quality, and (2) estimation of canopy water status through machine learning models based on multispectral and radar satellite data (Sentinel-1, Sentinel-2, Landsat 8).
By focusing on plant response rather than indirect climatic indicators, SatOri promises a scalable, farmer-friendly tool for adaptive irrigation under increasing water limitations.
The impact of weather variability on wheat and maize production: an improved early warning model for agricultural drought
This project developed a near-real-time seasonal prediction model for agricultural drought impact on maize and wheat in central Chile. Using high-resolution satellite data and environmental variables, the model helps anticipate crop losses and improve early warning systems.
The project analyzed high spatial resolution satellite data from Sentinel-2 and Landsat 8 using the sen2-Agri system to retrieve phenology and vegetation indices. A biomass estimation model was developed and validated with field measurements, allowing for reconstruction of historical yield records (2000-2022).
The prediction model uses satellite environmental data of soil moisture, precipitation, land surface temperature and biomass proxies, with a prediction lead-time of one to six months before the end of season. As the data used are publicly available globally, the models obtained could be adopted around the globe.