Conference Agenda

Abstract: Agricultural productivity is influenced by various natural factors, making its assessment critical for ensuring food security. In this study, we introduce a robust empirical index, the Land Productivity Index (LPI), designed to evaluate the relative productivity of agricultural areas globally. The LPI integrates key environmental parameters including climate, soil attributes, and water availability, providing a comprehensive assessment of agricultural potential at a spatial resolution of 250 meters.

Priority 1: Monitoring stressors and changing growing conditions The LPI methodology enables the monitoring of stressors and changing growing conditions by incorporating parameters such as mean monthly temperature, evapotranspiration, soil pH, organic carbon content, and water holding capacity. Through this holistic approach, we identify areas prone to environmental stressors, facilitating targeted interventions for sustainable agriculture.

Priority 2: Supporting increased resilience and risk management To support increased resilience and risk management, we extend the LPI methodology by integrating the ECOCROP database of the Food and Agriculture Organization (FAO). By customizing the LPI for key crops, we assess crop suitability across different spatial and temporal scales. This enables us to provide valuable insights into irrigation advice, crop monitoring, and risk mitigation strategies, enhancing agricultural resilience in the face of evolving environmental challenges.

Priority 3: Analyzing historical trends and variations Furthermore, our study analyzes historical trends and variations in land productivity by assessing historical average conditions and deviations from these averages over time. By identifying spatial shifts and temporal trends, we gain a deeper understanding of agricultural dynamics, empowering policymakers and stakeholders to make informed decisions for sustainable agricultural development.

Conclusion: Our integrated approach combining Earth observations with crop suitability analysis offers a comprehensive framework for assessing agricultural productivity and resilience under changing environmental conditions. By addressing the priorities of monitoring stressors, supporting resilience, and analyzing historical trends, our methodology contributes to enhancing agricultural sustainability and food security on a global scale.

This abstract introduces a comprehensive framework aimed at enhancing climate adaptation strategies for maize cultivation, focusing on the evolution of the Plan-T platform developed as proof-of-concept for the Food and Agriculture Organization of the United Nations (FAO) in the framework of the ESA project EO4YEMEN.

The methodology foresees three main steps: crop seasonality assessment, crop yield estimation and identification of the optimal planting date.

The assessment of crop seasonality integrates diverse data sources, including meteorological, agronomic, and satellite data. Advanced techniques such as the integration of ECOSTRESS data (Evapotranspiration) and climate stressor analysis contribute to more accurate variety suitability mapping.

The enhancement of crop productivity estimation involves refining the AQUACROP agronomic model, incorporating new input data, and calibrating model parameters. This process includes refining varietal parametrization, modeling yield response, and validating outputs with field data.

Variety selection and optimal planting date assessment are based on detailed analysis of physiologic responses to stressors and soil water balance, utilizing climate and soil moisture data as well as short-term high resolution weather forecast from ECMWF.

The Plan-T service is offered through a dedicated web application that can be exploited on both desktop and portable devices. It allows selecting the site of analysis down to a single field, or manually providing coordinates. Once done, it automatically shows the soil chemical parameters and ranks the maize variety based on climate suitability and productivity. It finally allows assessing the optimal planting date for each variety.

Overall, this framework facilitates informed decision-making for climate adaptation in maize cultivation, providing valuable insights into variety suitability, planting dates, and potential crop productivity. The integration of advanced methodologies and tools within the Plan-T platform enhances its usability and effectiveness in supporting agricultural practices amidst changing climatic conditions.

Recent studies on the impact of climate change on agricultural systems show significant changes that will occur in the global food production chain. New climatic conditions are expected to have a severe impact on the productivity of main crop types such as wheat, maize, soybean, and corn [1]. This will alter the global map of agriculture in the 21st century, facilitating crop migration and changes in agricultural practices. This process is ongoing and can already be observed in historical cropland maps [2].

To study this process, we employed climate velocity principles, allowing us to provide spatial-temporal characteristics of climate change and generate flow fields for climate parameters [3]. This technique was applied to a collection of 20 years of MODIS-based winter wheat maps to uncover spatial-temporal trends in winter wheat production changes and estimate the directions of winter wheat expansion and migration. This data on "Winter Wheat Velocity," together with Climate Velocity maps built based on ERA-5 Cumulative Biological-Active Temperature for winter crops, allowed us to link changes in wheat production with changes in climate and estimate the percentage of wheat migration and expansion caused not only by agricultural intensification but also by environmental changes in Europe.

References:

[1] Jägermeyr, Jonas, et al. "Climate impacts on global agriculture emerge earlier in new generation of climate and crop models." Nature Food 2.11 (2021): 873-885.

[2] Sloat, Lindsey L., et al. "Climate adaptation by crop migration." Nature communications 11.1 (2020): 1243.

[3] Loarie, Scott R., et al. "The velocity of climate change." Nature 462.7276 (2009): 1052-1055.

Rising temperatures, unpredictable precipitation patterns and extreme climate events are disrupting crop production globally. This is a major challenge for rice production, the key staple food for over half of the world’s population and a primary calorie source for millions of vulnerable people. The TAPAS platform combines remote sensing data and AI to monitor both environmental stressors and cultivation conditions of crops, with potential to positively impact on crop production by enabling decision-making for more resilient rice cropping systems. We identify, quantify and track crop production anomalies using a combination of supervised and unsupervised algorithms. We combine archival and real time Earth Observation (EO) data augmented by machine learning and deep learning techniques to monitor rice production quality, yielding up-to-date analyses specific to climate-impacted hotspots. Applying classification algorithms to Landsat imagery using periodic spectral information allows us to monitor changes or patterns in annual land use mapping that are specific to rice cropping cycles. Historical MODIS data (2000-2014) is used to train Long Short Term Memory Network models (LSTM) for predicting such cycles from 2015 onwards. This establishes a ‘climate baseline’ for previous decades which allows us to determine crop cycle performance relative to this baseline by examining differences in predicted and observed NDVI. Our method allows a remote-sensing only approach to measuring climate change adaptation of crops, validating the use of deep learning and EO in extracting key insights into climate adaptation strategies. The role of AI in EO to model climate effects and interacting geo-factors has implications for food production sustainability and decision making in reaching Sustainable Development Goal 2 targets. Overall, our approach has the potential to support evidence-based scaling of climate smart agriculture practices leading to more resilient agrifood systems, while providing critical crop productivity specific intelligence that can help mitigate food insecurity.

Rice is the staple food for half the world's population. Around 90% of the world's rice is produced in Asia, particularly in the main river deltas. However, due to their geographic location and low altitude, Asian deltas are classified among the regions most vulnerable to the impacts of climate change. Additionally, impacts are exacerbated by the activities of rapidly growing populations.

Within the Space Climate Observatory program, the objective of the VietSCO project is to study such impacts on the Mekong Delta, one of the most important rice deltas in the world. Sentinel-1 data was used to determine rice area, crop calendar, number of crops per year, etc. and to assess the changes that have occurred over the past decade. ALOS-PALSAR data was used to detect the flooding status of rice fields, which determines methane emissions.

It is seen that the habitat suitable for rice has undergone drastic changes. Options have been identified to reduce and adapt to the risks of increasing salinity intrusion, drought and flooding, and land submergence. Among mitigation practices, we highlight practices that mitigate methane emissions from rice. For the Global Methane Pledge, rapidly reducing methane emissions from rice fields is considered the most effective strategy to reduce global warming. Work is currently underway to evaluate the use of ALOS PALSAR, calibrated by automatic water level devices, to locate continuously flooded rice fields, where incentive actions must be taken to reduce methane emissions through proper drainage practices.

The work carried out aimed to arouse the interest of local and national decision-makers in the use of Earth observation to monitor the evolution of rice production under the impacts of climate change and anthropogenic pressures. To this end, a platform has been created to allow decision-makers to visualize current and projected impacts according to scenarios. Finally, we will discuss the potential for application of the approach developed to other large deltas in Asia.

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