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Hazard Risk Managment in Agriculture and Agroecosystems

In many parts of the world, weather represents one of the major uncertainties affecting performance and management of agricultural systems. Due to global climate changes the climatic variability and the occurrence of extreme weather events is likely to increase leading to substantial increase in agricultural risk and destabilisation of farm incomes. This issue is not only important for farm managers but also for policy makers, since income stabilisation in agriculture is frequently considered as a governmental task.

The aim of this session is to discuss the state of the art research in the area of analysis and management of weather-related risks in agriculture. Both structural and non-structural measures can be used to reduce the impact of climate variability including extreme weather on crop production. While the structural measures include strategies such as irrigation, water harvesting, windbreaks etc., the non-structural measures include the use of the medium-range weather forecast and crop insurance.

The topic is at the borderline of different disciplines, in particular agricultural and financial economics, meteorology, modelling and agronomy. Thus, the session offers a platform to exchange ideas and views on weather-related risks across these disciplines with the focus on quantifying the impact of extreme weather on agricultural production including impacts of climate change, analysis of financial instruments that allow reducing or sharing weather-related risks, evaluation of risk management strategies on the farm level, development of the theory of risk management and to exchange practical experiences with the different types of weather insurance.

This session has been promoted by:
• Natural hazard Early career scientists Team (NhET, https://blogs.egu.eu/divisions/nh/tag/early-career-scientists/)
• Boosting Agricultural Insurance based on Earth Observation data (BEACON, https://beacon-h2020.com/)
• Research Center for the Management of Agriculutral and Environmental Risks (CEIGRAM, http://www.ceigram.upm.es/ingles/)

Convener: Ana Maria Tarquis | Co-conveners: Anne Gobin, Margarita Ruiz-Ramos
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Wed, 28 Apr, 09:00–10:30

Chairpersons: Anne Gobin, Margarita Ruiz-Ramos, Ana Maria Tarquis

5-minute convener introduction

Freya Garry et al.

Assessments of current and future climate risk are required for adaptation planning to increase resilience and enable society to cope with future climate hazards. Here we identify case studies of compound hazard (involving heat and humidity) events of interest to the UK agricultural sector and present a framework for comparing the frequency and duration of compound events now to those projected in 50 years’ time. We use high resolution (12 km) simulations from the UK Climate Projections to explore how the frequency and duration of instances of potato blight and thermal heat stress to dairy cattle may change locally under RCP 8.5 emissions forcing. We combine hazard (temperature and humidity data) with vulnerability (specific threshold exceedance) and exposure (regional dairy cattle numbers/potato growing area) to estimate risk. Regions where most potatoes are grown, and where the potato blight risk is greatest in both the current and future climate, include the East of England, Yorkshire and the Humber and Eastern Scotland. By 2070, potato blight occurrences may increase by 70 % in East Scotland and between 20 - 30 % across the East of England, the Midlands and Yorkshire and the Humber. Assuming dairy cattle spatial distributions remain the same, the area of greatest risk now and in the future is South West England, with notable increases in risk across Northern Ireland, Wales, the Midlands, North West England and North West Scotland. Dairy cattle heat stress (using a temperature-humidity index) is projected to increase by over 1000 % in South West England, the region with the most dairy cattle. Finally, we consider projected changes to UK seasons, using 2018 as a template, where a cold spring followed by a warm/dry summer resulted in hay/silage shortages. In addition to reduced crop yields in 2018, cattle were kept inside for longer in the cold spring and in the warm/dry summer, due to heat stress and poor grass quality. UK Climate Projections indicate that the annual probability of cold spring/warm summer conditions will decrease in future, but the annual probability of longer dry/warm summers will increase. We conclude that the agricultural sector should consider suitable climate adaptation measures to minimise the risk of dairy cattle thermal heat stress, increased potato blight, and longer dry/warm summer conditions.

How to cite: Garry, F., Bernie, D., Davie, J., and Pope, E.: Future climate risk to UK agriculture from heat and humidity changes, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1169, https://doi.org/10.5194/egusphere-egu21-1169, 2021.

Katharina Enigl et al.

Climate change constitutes a main driver of altering population dynamics of spruce bark beetles (Ips typographus) all over Europe. Their swarming activity as well as development rate are strongly dependent on temperature and the availability of brood trees. Especially over the last years, the latter has substantially increased due to major drought events which led to a widespread weakening of spruce stands. Since both higher temperatures and longer drought periods are to be expected in Central Europe in the decades ahead, foresters face the challenges of maintaining sustainable forest management and safeguarding future yields. One approach used to foster decision support in silviculture relies on the identification of possible alternative tree species suitable for adapting to expected future climate conditions in threatened regions. 

In this study, we focus on the forest district of Horn, a region in Austria‘s north east that is beneficially influenced by the mesoclimate of the Pannonian basin. This fertile yet dry area has been severely affected by mass propagations of Ips typographus due to extensive droughts since 2017, and consequently has suffered from substantial forest damage in recent years. The urgent need for action was realized and has expedited the search for more robust alternative species to ensure sustainable silviculture in the area.

The determination of suitable tree species is based on the identification of regions whose climatic conditions in the recent past are similar to those that are to be expected in the forest district of Horn in the future. To characterize these conditions, we consider 19 bioclimatic variables that are derived from monthly temperature and rainfall values. Using downscaled CMIP6 projections with a spatial resolution of 2.5 minutes, we determine future conditions in Horn throughout the 21st century. By employing 20-year periods from 2021 to 2100 for the scenarios SSP1-26, SSP2-45, SSP3-70 and SSP5-85,  and comparing them to worldwide past climate conditions, we obtain corresponding bioclimatic regions for four future time slices until the end of the century. The Euclidian distance is applied as measure of similarity, effectively yielding similarity maps on a continuous scale. In order to account for the spatial variability within the forest district, this procedure is performed for the colder northwest and the warmer southeast of the area, individually seeking similar bioclimatic regions for each of these two subregions. Results point to Eastern Europe as well as the Po Valley in northern Italy as areas exhibiting the highest similarity to the future climate in this North-Eastern part of Austria.

How to cite: Enigl, K., Schlögl, M., and Matulla, C.: Trading space for time: Assessment of tree habitat shifts under climate change using bioclimatic envelopes, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2135, https://doi.org/10.5194/egusphere-egu21-2135, 2021.

Margarita Ruiz-Ramos et al.

Due to the latitude of the Iberian Peninsula, it is repeatedly affected by significant drought episodes. This has been the case of the events observed in the years 1979-1983, 1992-1995, 2005, or 2016-2017. In the historical period, the occurrence of droughts in the Peninsula has been closely linked to the natural variability of the climate itself, which is modulated by multiple factors, such as the surface temperature of the oceans, the polar ice cover, the Oscillation of the North Atlantic or the stratospheric circulation itself (e.g. Lorenzo et al., 2011). Within the context of global warming, the projected increase in temperatures is expected to have a direct impact on the recurrence and severity of droughts on the Iberian Peninsula.

Therefore, the objective of this work is to study the relationships between climatic variables that indicate a high risk of yield loss of rainfed cereals affected by drought, and their projection in the immediate future. This work has been framed in the area of ​​Castilla y León in the North Plateau of Spain.

The selected methodology consisted of the design of agrometeorological indices that allowed capturing the behaviour of the most relevant variables related to the response of the cereals to drought in the study area. For this purpose, meteorological station observations, observations in grid, and simulations of present and future climate generated by regional climate simulation models (EUROCORDEX RCMs, van Meijgaard et al., 2014), which were used to compute the indices after a bias correction. Finally, results maps were obtained.

A total of nine temperature and/or precipitation indices were designed and calculated for periods physiologically meaningful for the crop, both under present and future climate. A discussion of the potential consequences of the indices changes on winter cereal yields in Castilla y León was addressed.


Authors are grateful to Agroseguro funding through the project “Drought events in winter cereals in Castilla-León: risk analysis, trends and climate change”.


Lorenzo, M.N., Taboada, J.J., Iglesias, I., Gómez-Gesteira, M., 2011. Predictability of the spring rainfall in Northwestern Iberian Peninsula from sea surfaces temperature of ENSO areas. Clim. Change 107 (3–4), 329–341.

van Meijgaard, E., Moseley, C., Pfeifer, S., Preuschmann, S., Radermacher, C., Radtke, K., Rechid, D., Rounsevell, M., Samuelsson, P., Somot, S., Soussana, J.-F., Teichmann, C., Valentini, R., Vautard, R., Weber, B., and Yiou, P.: EUROCORDEX: new high-resolution climate change projections for European impact research, Reg. Environ. Change, 14, 563–578, https://doi.org/10.1007/s10113-013-0499-2, 2014.

How to cite: Ruiz-Ramos, M., Rodríguez, A., Saa-Requejo, A., Valencia, J. L., Villeta, M., and Tarquis, A. M.: Risk of drought for winter cereals in Castilla y León (N Spain) under current and future climate, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8459, https://doi.org/10.5194/egusphere-egu21-8459, 2021.

Guan Wang et al.

The windbreak system is a major component of successful agricultural systems in arid deserts throughout the world. Ulan Buh Desert is one of the eight biggest deserts in China, and the oases there offer residence and cropland for over 90% of the local residents. However, due to climate change and human disturbances, the Ulan Buh Desert continues spreading to the south, bringing more pressure on the windbreak systems there. Meanwhile, the Chinese government put much effort into greening the desert, establishing artificial shrubs to prevent dune movement and soil loss. How microclimate in the cropland-windbreak-desert system responded to human activities and climate change has rarely been studied. In this study, we investigated the microclimate change dynamics across the cropland-windbreak-desert transition zone during the past 38 years. Two 50 m climatological towers, located in the same distance inner and outside a shelterbelt, have continuously monitored climatic factors, including air temperature, soil temperature, relative humidity, precipitation, evaporation, layered wind speeds, etc., and aeolian erosion related factors, such as layered dustfall. The long-time fluctuations of the inside and outside climatic factors have been analyzed, and the global climate change data, local land-use history, as well as the record of afforestation activities implemented by government and local people, were also collected. The results revealed that both the inside and outside windbreak air temperatures and soil temperatures increased during the past 38 years, which agrees with the global warming phenomenon. The inner windbreak air temperature is consistently lower than the outer windbreak areas, and the temperature difference is biggest in summer and smallest in winter. However, the soil temperature difference between the outside and inner windbreak is unstable. In 1995, 2002, and 2004, the dune areas even had lower soil temperature than the inner cropland. The precipitation is 0.5~100.7mm higher in cropland and the evaporation is lower in cropland when comparing to outside dune areas, but their annual variations changed greatly. The wind speed and erosion rate are significantly lower in cropland than desert dune areas, and the seasonal change exhibited a bimodal curve pattern. The results suggest that the cropland-windbreak-desert transition zone responded to global climate change simultaneously. Although the shelterbelt still creates a favorable regional climatic condition for the cropland, the differences between the inner and outer windbreak areas narrowed during the past 10 years. The aeolian erosion rate reduced significantly in outside windbreak dune areas, which may largely attribute to the artificial Haloxylon ammodendron communities planted at the southeastern margin of the desert.

How to cite: Wang, G., Luo, F., Xin, Z., Li, J., and Xiao, H.: The 38 years of the microclimate change dynamics across a cropland-windbreak-desert transition zone in the Ulan Buh Desert, northern China, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11421, https://doi.org/10.5194/egusphere-egu21-11421, 2021.

Mário Gonçalves et al.

The chestnut trees are well adaptated to temperate and humid climates, with moderate annual thermal contrast and without long and severe summer droughts. Bioclimatic studies suggest that chestnut trees have special needs, including at least six months with average monthly air temperature above 10 ⁰C, total annual precipitation of 800 – 900 mm, and 25% of annual precipitation in summer. Weather is also determinant in the phenology of the species. For example, the suitable average air temperature range is: 13 – 15⁰C to initiate the phenological activity, 18 – 20⁰C for flowering, and 20 – 22⁰C for maturation. Therefore chestnut production is highly affected by adverse weather conditions and can be severely reduced by the occurrence of extreme weather/climate extremes: late frosts, heat waves, heavy rainfall, wind gusts, maximum air temperature lower than 25⁰C during flowering or above 32⁰C, which cause thermoinhibition of vegetative activity. Thus, it is important to characterize the chestnut producing regions in present and future climate and estimate how, when and where the weather conditions will be maintained or changed. For this study we used meteorological data from ERA5 for the 1981 – 2010 period and several GCM-RCM simulations from CORDEX Bias-adjusted RCM data for 2011 – 2100 period to assess the climate for current and two future scenarios (RCP 4.5 and RCP 8.5). The meteorological variables selected for this study have been identified in previous studies as having the greatest influence in the phenological activity of the chestnut tree and on the chestnut productivity. The results include the identification of the regions where: (i) the variables will have significantly different statistical distributions in the future; (ii) will be necessary to adopt hazard risk management and climate adaptation measures, including substitution by other varieties more adapted to future conditions or the development of genetic improvement programs; and, (iii) the identification of new production areas.

How to cite: Gonçalves, M., Amraoui, M., Laranjo, J., and Pereira, M.: Climate change in chestnut producing regions in Portugal, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16105, https://doi.org/10.5194/egusphere-egu21-16105, 2021.

Marcos Roberto Benso et al.

In the last decades, we have witnessed increasing losses on crop yield due to an increase in magnitude and frequency of hydrological extremes such as droughts and floods. These hazards promote systematic and regressive impacts on the economy and human behavior. Risk transfer mechanisms are key to cope with the economic impacts of these events, therefore safeguarding income to farmers and building resilience to the overall sector. The index-based insurance establishes an index that can be monitored in real or near-real-time, which is associated with losses to a specific agent. While the manifestation of the causality hazard to exposure and exposure to damage and its mathematical representation in cash flow equations is a hard task, incorporating an objective and transparent index adds up a new challenge to this modeling framework. Moreover, past events that have been used as the main guide to evaluating expected losses given risk can no longer offer an accurate risk estimation due to environmental changes. This work aims to tackle the hydrologic extremes risk transfer modeling in irrigated agriculture to obtain optimized premium values and parameters of an insurance fund for irrigated agriculture in Southeastern Brazil. This study will be developed in the Piracicaba, Jundiaí, and Capivari river basin, also known as PCJ catchment in the states of São Paulo and Minas Gerais, Brazil. The region, with approximately 5 million inhabitants, is considered one of the most important in Brazil due to its economic development, which represents about 7% of the National Gross Domestic Product (GDP). The Hydrologic Risk Transfer Model of the Hydraulic and Sanitation department of the University of São Paulo (MTRH-SHS) will be used to obtain optimized premium values. The main index variable is streamflow fitted to extreme value theory distribution for low and high flows. To evaluate climate change and land-use change scenarios, Regional Climate Models (RCMs) and land use projections will be related to streamflow in a hierarchical Bayesian framework. Synthetic data will be then simulated according to scenarios previously defined in a Monte Carlo approach. The hazard-damage function will be obtained by total crop yield and revenue per municipality, then the relationship between the index and expected losses is determined in an empirical equation. Finally, a cash flow computation is run with synthetic data obtaining optimized premiums in a way to minimize fund storage values. We expect to provide further evidence of the feasibility of actuarially fair premium values for the agents in the sector considering global phenomena of climate change and land-use change. Results will support climate change adaptation plans and policy as well as contribute to methods for estimating risk in a changing environment.

How to cite: Benso, M. R., Gesualdo, G. C., Mendiondo, E. M., Ribbe, L., and Nauditt, A.: Developing resilience to hydrologic extremes in irrigated agriculture through risk transfer mechanisms in the context of southeastern Brazil, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6781, https://doi.org/10.5194/egusphere-egu21-6781, 2021.

Donguk Seo

Agricultural reclaimed land in Korea is increasingly being used for horticulture, grains, livestock, etc. However, soil of reclaimed land located in coastal lowland have so high salinity, poor fertility, high possibility of pollution that farming is difficult. Therefore, it is needed to promote desalination and fertility of soil and to reduce environmental burden through natural circulation farming. Therefore, we presented sustainable eco-friendly natural circulation model of agricultural resources in reclaimed land. The test complex was planned to apply circulation of energy and resources between horticulture and livestock focusing on Hanwoo, Korean-bred cattle. After comparing and analyzing the pH, organic matter, effective phosphate, potassium, calcium, magnesium, and electrical conductivity of the reclaimed land soils, and the general range soils, the appropriate number of Korean cattle was calculated. The estimated livestock manure of Korean cattle is used for liquid fertilizer, composting and energy. The total manure discharge can be calculated according to the area from one manure discharge. In addition, Pellet from cattle's manure was planned to be fueled and used as heating energy for horticulture facility. The greenhouse can be sized to a scale that meets the greenhouse's total heating load by calculating the total amount of energy generated from the manure. Therefore, plastic greenhouse-type horticultural complexes and livestock complexes including fuel facility using manure pellet are planned. So, natural circulation is completed as the manure of livestock provides the organic matter to the farmland and heating energy to the greenhouse. Additionally, agricultural product processing, sales and distribution centers, themed landscape agricultural complexes, ecological parks, agricultural tourism facilities, and observation facilities were arranged.

How to cite: Seo, D.: Test Planning for Natural Circulation Farming Model in Agricultural Reclaimed Land, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-488, https://doi.org/10.5194/egusphere-egu21-488, 2021.

Emanuel Lekakis et al.

Agricultural Insurance (AgI) sector is expanding on a global scale and is projected to grow by €50 B, by 2020. This rapid growth is driven by a set of fundamental structural changes directly affecting the agricultural sector like more frequent and severe extreme weather events, growing global population and intensification of production systems. Insurance solutions are set to grow in importance for agricultural management, given that agriculture will continue to be increasingly dependent on risk financing support. However, the development and provision of insurance services/products in the agricultural sector is generally low as compared to other sectors of the economy, and in their majority, suffer from low market penetration.

In that frame, the BEACON toolbox was born, that aims to provide insurance companies with a robust and cost-efficient set of services that will allow them i) to alleviate the effect of weather uncertainty when estimating risk of AgI products; ii) to reduce the number of on-site visits for claim verification; iii) to reduce operational and administrative costs for monitoring of insured indices and contract handling; and iv) to design more accurate and personalized contracts. Specifically, BEACON scales-up on EO data and Weather Intelligence components, couples them with blockchain, to deliver the required functions for Weather Prediction and Assessment and Smart Contracts and offer the required services:

  • Crop Monitoring, which provides contract profiling and crop monitoring data together with yield estimations.
  • Damage Assessment Calculator, which supports AgI companies in better assess and calculate damage to proceed with indemnity pay-outs of claims.
  • Anti-fraud Inspector, which allows AgI to automatically check the legitimacy of a claim submitted.
  • Weather Risk Probability, which provides probabilities maps of extreme weather events that may occur in the upcoming season.
  • Damage Prevention/ Prognosis – Early Warning System, which provides extreme weather alerts to AgI providers and their customers.

This work focuses on the Damage Assessment Calculator component. It provides an approach using different types of EO data, implemented in the operational workflow of BEACON that can be used by AgI companies to improve the prediction and crop loss assessment due to drought and hailstorms.



This  project  has  received  funding  from  the  European  Union's Horizon 2020 Research and Innovation programme under grant agreement No 821964 (BEACON).

How to cite: Lekakis, E., Tarquis, A. M., Kotsopoulos, S., Mygdakos, G., Dimitrakos, A., Tsioutsia, I. M., Rivas-Tabares, D., and Simeonidou, P.: Evaluation of Earth Observation Products and Their Potential for Crop Damage and Crop Loss Assessment. The Case of Beacon Project., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10829, https://doi.org/10.5194/egusphere-egu21-10829, 2021.

Chandra Taposeea-Fisher et al.

Part of ESA’s Digital Twin Earth Precursor projects, our project focuses on supporting ESA in the definition of the concept of a Digital Twin Earth, and establishing a solid scientific and technical basis to realise this. The project, run by CGI and in close collaboration with Oxford University Innovation, Trillium & IIASA, has a focus on developing a Food Systems Digital Twin, taking on board interdisciplinary systems through the biosphere, atmosphere, and hydrosphere systems. These in turn would allow for new interdisciplinary insights for policies dealing with climate, food production and sustainability. The project is looking at a use case with the prominent use of AI processing, challenges of model integration, ingestion of socio-economic as well as physical measurements, end-to-end chain providing decision support outputs, all with innovation at each stage, and working closely with a series of stakeholders.

The purpose of our use case is to demonstrate the value of the Digital Twin Earth concept to the scientific community, by integrating the outputs of novel algorithms. We will be using selected machine learning extreme precipitation models feeding Global Gridded Crop Models, and after a regional downscaling exercise, the integration into cropland land use and pricing. By taking these steps, the benefits include improvement in routine monitoring with regular seasonal progress, short term policy development including responses to crop shortages due to extremes, and aiding in long term policy development to apply appropriate incentives. The purpose of the architecture and integration within the preparation of the demonstration is to support the use case and draw conclusions for the roadmap. These developments will be based on stakeholder consultations and the drawing together of differing model elements.

This Digital Twin Earth is an exciting project bringing together EO experts, Earth System Scientists, industry, AI experts, modellers, ICT experts and user community. It aims to establish the initial building blocks of an ambitious initiative, and, based on the prototyping activities, to develop a scientific and technology roadmap for the future, addressing current limitations. It ties in closely to both the European Space Agency’s and European Commission’s plan to create a series of interdisciplinary Digital Twin Earths with associated boundary conditions, in order to offer services to public sector users for developing, monitoring and assessing the impact of proposed policy and legislative measures concerning the environment and climate.

How to cite: Taposeea-Fisher, C., Whitelaw, A., Earl, J., Cullingworth, C., Jackman, S., Obersteiner, M., Watson-Parris, D., Gal, Y., Khabarov, N., Folberth, C., Orduña-Cabrera, F., Parr, J., and Silverberg, L.: ESA Digital Twin Earth Precursor: Food Systems, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6182, https://doi.org/10.5194/egusphere-egu21-6182, 2021.

Gennady Bracho Mujica et al.

Extreme events, such as drought, heat and/or frost are among the major weather-related causes of yield reduction and crop failure worldwide. Changes in the frequency and intensity of such weather extremes affect the shape and scale of yield distributions. Wheat growers, in Australia, are particularly vulnerable to climate due to its high variability. Risks of both, extremely high or low temperatures and water stress occurring simultaneously or at different crop stages within the growing season (May-October, e.g. frost mid-season, drought during the season and heat towards the end) often lead to yield reductions, or sometimes even to crop failure. In this study, we focused on assessing the frequency and impact of these relevant extreme weather events (i.e. drought, heat and frost) affecting wheat production in Australia. Specifically, we used a widely used and calibrated crop model (APSIM) to simulate wheat grain yield, and determine probability density functions (PDFs) of grain yield and crop failure. Chances of crop failure due to these extreme events are explored for the recent past (1991-2020) and the longer-term historical past (1901-1990). Key adaption strategies to minimise the impacts of these extreme events, and reduce crop failure risk are assessed in this study, including early sowing and cultivar choice. Our findings are in line with recent studies, indicating that drought and heat are major risk factors contributing to reduced yields or crop failure. However, due to the timing, frequency and impacts of frost events on wheat productivity, frost also remains a relevant risk for the wheat industry in Australia.

How to cite: Bracho Mujica, G., Hayman, P., Sadras, V., Ostendorf, B., Ferreira C. R., N., Abdulai, I., and Rötter, R.: Major weather-related risks to crop performance along the Australian wheat belt for recent past and longer-term historical weather records, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15335, https://doi.org/10.5194/egusphere-egu21-15335, 2021.

Beatrice Monteleone et al.

Evaluating the impacts of weather events on the agricultural sector is of high importance. Weather has a huge influence on crop performance and agricultural system management, particularly in those countries where agriculture is mainly rainfed. Climate change is expected to further affect farmers’ incomes since the risk of extreme weather events with a relevant impact on crop yields is predicted to increase.

Appropriate strategies to deal with the economic impacts of agriculture need to be developed, to enable farmers to quickly recover after a disaster. In this context, weather-based index insurance (also known as parametric insurance) plays a key role since it allows farmers to receive financial aid soon after a disaster occurs.

This study evaluates the applicability of crop models run with gridded data in the framework of index-based insurance to assess their added value in providing estimations of crop yield in case of drought events.

At first, the cropland area is identified using satellite data on Normalized Difference Vegetation Index (NDVI) and Leaf Area Index (LAI) retrieved from various sources, such as Sentinel and Landsat. Crop Type maps are then produced to identify the location of the different crops grown in a region. Then, weather data coming from stations are exploited to run the AquaCrop crop model and estimate the crop yield for the areas near the weather stations.

Since in many countries weather stations are often missing or do not record continuously, the AquaCrop model is also run with gridded data coming from reanalysis, specifically ERA, which is a product released by the European Centre for Medium Range Weather Forecast and has the advantage to provide daily estimation of  multiple weather parameters on a 0.25° grid. In addition, ERA5 has a short latency time (in the order of days) and thus allows a near-real time monitoring of the crop growing season. The AquaCrop outputs obtained when the model is run with the station data are then compared to the ones obtained when the model is run with gridded data. The performance of the two model configurations (weather parameters coming from stations or from ERA5) in estimating yield reductions during drought events, previously identified using the Probabilistic Precipitation Vegetation Index (PPVI), are evaluated.

The framework was applied in the context of the Dominican Republic, a Caribbean country in which 52% of the national territory is devoted to agriculture. The Dominican agricultural industry has as main products cocoa, tobacco, sugarcane, coffee, cotton, rice, beans, potatoes, corn and bananas. Results shows that gridded data can be a valuable tool to provide near-real time estimates of the crop growing season and thus help in forecasting final crop yields in near-real time.

How to cite: Monteleone, B., Cesarini, L., Figueiredo, R., and Martina, M.: Applicability of crop models in the context of parametric insurance – a Caribbean case study, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10120, https://doi.org/10.5194/egusphere-egu21-10120, 2021.

Riccardo Giusti et al.

Globally, about a third of all losses related to natural hazards are due to flooding. Many studies focused their attention on the estimation of flood damages to buildings and infrastructures. However, floods cause significant losses to the agricultural sector too and negatively affect rural economies due to their impacts on agricultural productivity.

Several tools to quantify flooding economic impacts on the agricultural sector have been proposed, such as the AGRIDE-c conceptual model, and the Joint Research Centre (JRC) depth-damage functions. However, the tools have rarely been validated against data collected from surveys.

The aim of this study is the comparison between the flood economic impacts on agriculture computes using both AGRIDE-c and the JRC tool and the ones retrieved from surveys.

A questionnaire for estimating flood economic impacts on agriculture was prepared and submitted to farmers shortly after the flooding event. The selected case study area was the town of Nonantola (near the city of Modena, Northern Italy), where a flooding event occurred on 6th December 2020. The flood was caused by the collapse of about 80m levee portion along the right bank of Panaro River resulting in an inundated area around 2000 hectares. The flood involved the Nonantola town where residential buildings and an active industrial area are located, although the dominant land use is agricultural land. The main local crops are represented by forage, wheat, vineyards, fruits (pears and plums) and sugar beet.

The questionnaire is divided into four main sections: The first section is related to the generic information on the farm, the second section to the data on the inundation and damage to crops, the third section to the information on past flood events and risk mitigation strategies eventually adopted during past and present events, the fourth section data to the insurance coverage.

Two existing crop damage models (AGRIDE-c and the JRC) were calibrated using three types of data: crop yields, crop selling prices and crop cost of production. Crop yields were obtained from the Italian National Statistical Institute (ISTAT), crop selling prices and costs of production were instead available from official sources such as ISMEA and Coldiretti (Italian association of farmers).

Finally, the proposed approach will allow the comparison between the damages experienced by farmers evaluated from questionnaires and the damages estimated by the two models in order to evaluate how the models simulate data directly collected from the field surveys.

How to cite: Giusti, R., Monteleone, B., Borzì, I., and Martina, M.: Flood impacts on Agriculture: The case study of Nonantola 2020 Flood event. , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10256, https://doi.org/10.5194/egusphere-egu21-10256, 2021.

Anne Gobin et al.

Water availability and using the available water in a well-considered manner is becoming increasingly more important for farmers. An increase in dry summers (Gobin, 2018) confirms that in the future irrigation will have to take place in a smart manner, particularly in the water demanding horticultural sector. In times of water scarcity, governments may issue a ban on water extraction from natural water bodies. This on-going research investigates to what extent the supply of alternative water sources, such as treated waste water from domestic use or food processing , can be used for irrigation. In addition to the availability, the quality of irrigation water determines its suitability for crop utilisation. Treated domestic waste water may contain pathogens rendering the irrigated crop unfit for fresh consumption, whereas treated waste water from food companies may contain high salt concentrations affecting soil and crop health. The water demand was investigated on wastewater irrigated field trials and on irrigated farmers’ fields. Irrigation trials with various types of treated waste water elucidated the effects of these water sources on the crop yield, crop quality and the long-term impact on the soil quality. Soil moisture sensors were combined with a crop model, satellite images and meteorological information to monitor crop growth and performance of potato, spinach and cauliflower in on-farm conditions. The regional water demand for all irrigated crops was calculated with a water balance model based on actual evapotranspiration (Zamani et al., 2015) and linked to the supply of waste water sources in an online viewer, which makes it possible to promote water coalitions in regions where the water demand is high.


The research was funded by the Flemish Agency for Innovation and Entrepreneurship in Belgium under contract agreement HBC.2017.0817. 


Gobin, A., 2018. Weather related risks in Belgian arable agriculture. Agricultural Systems 159: 225-236. https://doi.org/10.1016/j.agsy.2017.06.009

Zamani, S., Gobin, A., Van de Vyver, H., Gerlo, J., 2015. Atmospheric drought in Belgium - Statistical analysis of precipitation deficit. International Journal of Climatology 36(8): 3056–3071. https://doi.org/10.1002/joc.4536


How to cite: Gobin, A., Boeckaert, C., Coudron, W., De Cuypere, T., De Swaef, T., Huits, D., Lootens, P., and Pollet, S.: Bridging the water demand gap with wastewater irrigation, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13617, https://doi.org/10.5194/egusphere-egu21-13617, 2021.

Andrea Urgilez-Clavijo and Ana M. Tarquis

The expansion of the agricultural frontier is a process that has been affecting natural ecosystems, driving landscape fragmentation, and promoting habitat loss from 1990 in the Ecuadorian Amazon. Characterizing spatial patterns of the expansion of agricultural frontier can provide valuable data to take forward trade-offs in areas with exacerbated expansion rates and habitat loss (Urgilez-Clavijo et al., 2020). The aim of this work is to identify and characterize the spatial patterns of the expansion of the agricultural frontier in Ecuador and provide an alternative to setting the priority areas.

With this purpose, an image analysis approach was applied to identify process patterns using classified images from 1990 to 2020. A statistical analysis of the agricultural expansion dynamics is performed in the Amazon region accumulating the land use information. Complementary to this, we used a soil map to detect a correlation of the process to soil types. Then the Intensity Analysis (IA) was implemented to characterize and visualize the spatio-temporal rates of the expansion process. This method allows identifying areas in which the process is faster and active.

The results show distinct patterns of agricultural expansion in the Amazon region, especially from Andean hill slopes to the primary forest. These processes are in part explained by soil type suitability, transportation network development, and urban expansion. The spatial priorities of the expansion of the agricultural frontier are identified from two sources, i) from intensity analysis graphs and ii) from regional maps. The spatial characteristics and identification of spatial priorities of the expansion of the agricultural frontier will bring valuable information to policymakers to achieve SDG 15th of the 2030 Agenda in Ecuador.

Keywords: expansion of agricultural frontier, Intensity Analysis, priority areas, image analysis, patterns


Urgilez-Clavijo, A., J. de la Riva, D. Rivas-Tabares and A.M. Tarquis. Linking deforestation patterns to soil types: A multifractal approach. European Journal of Soil Science, https://doi.org/10.1111/ejss.13032


The authors acknowledge support from Project No. PGC2018-093854-B-I00 of the Spanish Ministerio de Ciencia Innovación y Universidades of Spain and the funding from the Comunidad de Madrid (Spain), Structural Funds 2014-2020 512 (ERDF and ESF), through project AGRISOST-CM S2018/BAA-4330

How to cite: Urgilez-Clavijo, A. and Tarquis, A. M.: Agricultural spatial expansion in Ecuador through Intensity Analysis, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13547, https://doi.org/10.5194/egusphere-egu21-13547, 2021.

Antonio Saa-Requejo et al.

Soil erosion is an important process of consideration in different erosion risk models and in planning soil conservation. Common erosion models, such as the USLE and its derivatives are widely used. In this context, the slope length is the variable with the most difficulties due to the different scales and procedures available that lead to very different results. Furthermore, many of the calculation procedures are based on a hydrological network definition that poses many problems in areas with a complex topography.

We propose an algorithm implemented in GIS, returning to the original field perspective form defined by the USLE and RUSLE, which is detached from the hydrological network definition. The calculation procedure is based on 5 m DEM and defines overland water flow at the field scale.

This method has been applied in three areas with different climate and geomorphology. The results are similar to those derived from aerial photograph observation.


Honghu Liu, Jens Kiesel, Georg Hörmann, Nicola Fohrer. (2011). Effects of DEM horizontal resolution and methods on calculating the slope length factor in gently rolling landscapes. Catena, 87, 368–375

Renard, K.G., Foster, G.R., Weesies, G.A., Mc. Cool, D.K y Yoder, D.C. (1997). Predicting Soil Erosion by Water: A Guide To Conservation Planning With The Revised Universal Soil Los Equation. Agricultural Handbook 703. USA: US Department of Agriculture.


Authors are grateful to Authors are grateful to Agroseguro funding this research.

How to cite: Saa-Requejo, A., Sevilla, P., Tarquis, A. M., and Gobin, A.: Computing slope length (USLE): return to original definitions., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13525, https://doi.org/10.5194/egusphere-egu21-13525, 2021.

Gordon Gilja et al.

This paper presents the concept of the project “Advanced monitoring of soil salinization risk in the Neretva Delta agroecosystem” (Delta Sal). Aim of the project is to develop and implement an advanced system for monitoring, forecasting and reporting the water and soil conditions in the Neretva Delta agroecosystem that is primarily used for agriculture. Selected pilot location is specific due to its biodiversity – water network within the delta consists of surface irrigation and drainage canal network, carst aquifer dominated by the tidal regime while also replenished by the freshwater from the upstream river flow, all of which are used for citrus fruits production while at the same time influencing the water regime of adjacent protected salt marshes ecosystem. Neretva Delta is dominated by the traditional farming methods practiced in the polder systems. Salt water intrusion is present in the entire delta, which is reflecting on the irrigation water quality and subsequently on the agricultural production of citruses that are salt-sensitive horticultural crops. Extensive spatial and temporal monitoring of water quality data through multisensory monitoring stations will be used for development of guidelines for salt stress alleviation in citrus fruits. This paper presents the outline of the project, methodology of analysis and selection of representative agricultural parcels for the research, rationale of farmer’s current decision-making that affects the agricultural landscape pattern and proposed monitoring network. Monitoring is focused on continuous real-time measurements of surface water levels and index water velocity using radars, shallow and deep piezometers for monitoring of ground water levels, rain gauges, multiparameter water quality measurements (dissolved oxygen, water depth, electrical conductivity, total dissolved solids, salinity, pH, oxidation reduction potential, temperature, nitrate and chloride). Data is transmitted in real-time to the cloud-based interface for remote access. Integrated data management will be used in the upcoming project stages for analysis of salt water intrusion on Neretva Delta agricultural production. Final outcome of the project are guidelines for Neretva Delta management with the future outlook in the climate change context, compliant with UNFCCC convention under which this area falls into one of the most vulnerable areas in Croatia.



„This work has been supported in part by the European Regional Development Fund under the project Advanced monitoring of soil salinization risk in the Neretva Delta agroecosystem (KK.“

How to cite: Gilja, G., Kuspilić, N., Romić, D., Zovko, M., and Harasti, A.: Advanced monitoring of soil salinization risk in the Neretva Delta agroecosystem, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-203, https://doi.org/10.5194/egusphere-egu21-203, 2020.

Mohamed Amine Ben Rhaiem et al.

The agriculture sector in Tunisia plays a vital role in the Tunisian economy with 6% of the country's exports earning, 12.6% of GDP and almost one
quarter of the country's labor force. However, Tunisian agriculture is still increasingly exposed to a variety of vulnerabilities and uncertainties including in particular the climate variability such as drought and floods. In fact, Rainfall quantity and its geographic distribution are the main drivers of water productivity and agriculture production and a predominant key factor in the overall agriculture hazard risk management processes. This paper uses the daily open rainfall data from the national observatory of Tunisian agriculture (ONAGRI) to develop an ETL (Extract,Transform and load) tool to automatically spatialize and load the historical data into a big data platform by continuously incrementing the new daily disseminated records. In addition, this paper applies the Voronoi spatial analysis model to estimate rainfall measures for the newly added spatial units using VGI data from OSM world mapping project. Then, based on these spatial estimations, the paper examines the feasibility of applying ARIMA (Auto Regressive Integrated Moving Average) for time series forecasting by comparing it with deep learning methods ANN (Arti cial Neural Network) and LSTM (Long Short Term Memory) in order to predict the rainfall values corresponding to particular agriculture area belonging to a Tunisian region. Our experimental results showed that prediction accuracy increased with LSTM model comparing to the other models for the rainfall time series forecasting embedded now with geographic location.

How to cite: Ben Rhaiem, M. A., Ben Hassine, A., Ouertani, A., and Riadh Farah, I.: A hybrid rainfall prediction model for Tunisian agriculture regions based on OSM data, Voronoi spatial analysis and Long Short Term Memory deep learning, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12993, https://doi.org/10.5194/egusphere-egu21-12993, 2021.

Ernesto Sanz Sancho et al.

Rangeland and agricultural landscapes are complex and multifractal based on the interaction of biotic and abiotic factors such as soil, meteorology, and vegetation. The effects of land-uses on these areas modify their characteristics and dynamics.  The use of Normalized Difference Vegetation Index (NDVI) and NDVI anomalies (NDVIa) from satellite time series can effectively aid on understanding the differences among rangeland uses and types.

Multifractal detrended fluctuation analysis (MDFA) focuses on measuring variations of the moments of the absolute difference of their values at different scales. This allows us to use different multifractal exponent such as generalized Hurst exponent (H(q)), and the scaling exponent (ζ(q)) to characterize each area.

We collected the time series using satellite data of MODIS (MOD09Q1.006) from 2002 to 2019. One area from southeastern Spain (Murcia province) of 6.25 Km2 were selected. This area comprises 132 pixels with a spatial resolution of 250 x 250 m2 and a temporal resolution of 8 days. This area represents a mix of tree crops rainfed and irrigated, rainfed herbaceous crops, and grazelands with shrubs and/or tree coverage.

MDFA was used on every pixel of the study area and H(q) was plotted and compared. Our results report different exponent behaviours for diverse rangeland type or use. Within the same vegetation type, MDFA can allow us to distinguish among pixels, such as the top central part of our area, where different persistence levels are found for the same land use. Comparing the Hurst exponent (H(2)) of NDVI and NDVIa also suggest a difference of influence on the multifractal character of long-range correlations.

We conclude that MDFA is a good tool to characterize arid rangelands spatial heterogeneity, particularly for rangeland with different vegetation types. It can be used to monitor and manage arid rangeland. It can be useful for policy-makers for short- and long-term solutions.

Acknowledgements: The authors acknowledge the support of Project No. PGC2018-093854-B-I00 of the Ministerio de Ciencia Innovación y Universidades of Spain, “Garantía Juvenil” scholarship from Comunidad de Madrid, and the financial support from Boosting Agricultural Insurance based on Earth Observation data - BEACON project under agreement Nº 821964, funded under H2020EU, DT-SPACE-01-EO-2018-2020.

How to cite: Sanz Sancho, E., Saa-Requejo, A., Diaz-Ambrona, C. G., Ruiz-Ramos, M., and Tarquis, A. M.: Multifractal analysis of spatial heterogeneity in Spanish arid rangelands, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2698, https://doi.org/10.5194/egusphere-egu21-2698, 2021.

David Rivas-Tabares and Ana María Tarquis Alfonso

Rainfed crops as cereals in the semiarid are common and extensive land cover in which climate, soils and atmosphere interact trough nonlinear relationships. Earth Observations coupled to ground monitoring network allow to improve the understanding of these relationships during each cropping season. However, novel analysis is required to understand these relationships in larger periods to improve sustainability and suitability of the productive areas in the semiarid.

The aim of this work is to use a joint multifractal approach using vegetation indices, precipitation, and temperatures to analyze atmosphere-plant nonlinear relationships. For this, time series of 20 cropping seasons were used to characterize these relationships in central Spain. The Generalized Structure Function and the derived Generalized Hurst Exponent analysis were implemented to investigate precipitation, vegetation indices and temperature time series. For this, an exhaustive selection based on land use and a land cover change analysis was performed to detect plots in which cereal crop sequences are dedicated to barley and wheat over the period 2000 to 2020.

As a result, two agro zones were characterized by different multifractal properties. Precipitation series show antipersistent characteristics and fractal properties between zones while original vegetation indices show trending behavior but shifted between analyzed zones. Nonetheless, soils and rainfall events can vary interannual conditions in which the crop is developing. For vegetation indices long-term series the trend is persistent. Even so, the dynamics of vegetation indices also provide more information when annual patterns are extracted from the series, exhibiting fractal properties mainly from rainfall pattern of each zone. Finally, in this case, the joint multifractal analysis served to characterize agro zones using earth observation and climate data for extensive cereals in Central Spain.


Rivas-Tabares D., Tarquis A.M. (2021) Towards Understanding Complex Interactions of Normalized Difference Vegetation Index Measurements Network and Precipitation Gauges of Cereal Growth System. In: Benito R.M., Cherifi C., Cherifi H., Moro E., Rocha L.M., Sales-Pardo M. (eds) Complex Networks & Their Applications IX. COMPLEX NETWORKS 2020 2020. Studies in Computational Intelligence, vol 943. Springer, Cham. https://doi.org/10.1007/978-3-030-65347-7_51


The authors acknowledge support from Project No. PGC2018-093854-B-I00 of the Spanish Ministerio de Ciencia Innovación y Universidades of Spain and the funding from the Comunidad de Madrid (Spain), Structural Funds 2014-2020 512 (ERDF and ESF), through project AGRISOST-CM S2018/BAA-4330 and the financial support from Boosting Agricultural Insurance based on Earth Observation data - BEACON project under agreement Nº 821964, funded under H2020_EU, DT-SPACE-01-EO-2018-2020.

How to cite: Rivas-Tabares, D. and Tarquis Alfonso, A. M.: Joint multifractal approach to characterize nonlinear relationships of climate and cereal growth in semiarid, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13498, https://doi.org/10.5194/egusphere-egu21-13498, 2021.

Andrés Felipe Almeida Ñauñay et al.

Grasslands are one of the world's major ecosystems groups many of them are now being acknowledged as having a multifunctional role in producing food and rehabilitating croplands, in environmental management and cultural heritage. Multiple studies showed pasture grasslands as a complex agroecological system, depending on multiple variables with a nonlinear dynamic greatly affected by climate fluctuations over time. Remote sensing methods proved to be one of the most effective techniques for monitoring variations over wide areas. In this line, vegetation indices (VIs) demonstrated to be an appropriate indicator of vegetation cover condition. This study aims to perform a method to visualize and quantify the complexity between semiarid grasslands and climate. With this goal, VIs and climate time series are analysed simultaneously with non-linear techniques to reveal the dynamic behaviour of both series over time and their interaction.

A semi-arid grassland area characterized by a Mediterranean climate with a continental character and low precipitation throughout the year were chosen. VIs time series were constructed from MODIS TERRA (MOD09Q1.006) product. Multispectral images composed by 8-days were acquired from 2002 till 2018 and four pixels with a spatial resolution of 250 x 250 m2 were chosen in the selected area. Normalized Difference Vegetation Index (NDVI) and Modified Soil-Adjusted Vegetation Index (MSAVI) were calculated based on these images. Temperature and precipitation series were acquired from a near meteorological station and adapted to 8-day time step.

Cross-Recurrence plots (CRP) and recurrence quantification analysis (RQA) were performed to analyse the climate and vegetation dynamics simultaneously. To achieve this goal, several measures of complexity were computed, such as Determinism (DET), average diagonal length (LT) and entropy (ENT).

Our results showed different CRPs depending on the climate variable and the utilized VIs. Temperature and VIs CRPs showed a periodical pattern, implying the temperature seasonality over time. In contrast, precipitation and VIs CRPs showed more chaotical behaviour, due to the occurrence of extreme events and seasonal shifts. These results are quantified by the DET and ENTR values.

Our results indicate that temperature and precipitation present a dynamical complexity that is revealed in VIs response. Both indices showed different results of complexity measures, implying that MSAVI has a higher complexity than NDVI. This fact is probably due to the addition of a variable soil adjustment factor. Consequently, MSAVI should be considered as a potential alternative to NDVI in semiarid areas.


Almeida-Ñauñay, A. F., Benito, R. M., Quemada, M., Losada, J. C., & Tarquis, A. M. Complexity of the Vegetation-Climate System Through Data Analysis. In International Conference on Complex Networks and Their Applications. Springer, Cham., 609-619, 2020


The authors acknowledge support from Project No. PGC2018-093854-B-I00 of the Spanish Ministerio de Ciencia Innovación y Universidades of Spain and the funding from the Comunidad de Madrid (Spain), Structural Funds 2014-2020 512 (ERDF and ESF), through project AGRISOST-CM S2018/BAA-4330 and the financial support from Boosting Agricultural Insurance based on Earth Observation data - BEACON project under agreement Nº 821964, funded under H2020_EU, DT-SPACE-01-EO-2018-2020.

How to cite: Almeida Ñauñay, A. F., Benito Zafrilla, R. M., Quemada Sáenz-Badillos, M., Losada, J. C., and Tarquis Alfonso, A. M.: Revealing climate and vegetation indices interactions through Cross Recurrence Techniques, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12723, https://doi.org/10.5194/egusphere-egu21-12723, 2021.

Ana M. Tarquis et al.

Agroindustry in South and Central America is positioned as a traditional production sector, where exists a need for integration of processes for the implementation of contingency measures in a timely manner against events that create a risk for crops. Diseases affecting agricultural sectors are often closely related to weather conditions and crop management. In particular, for the coffee production, the Coffee Leaf Rust (CLR) is a disease that affects quality and production costs for farmers greatly. 

Detecting the patterns that affect the disease can lead to early actions that lessen its impact. In this sense, some researchers in the sector have focused their efforts on determining over time the relationships between weather conditions and agronomic properties of crops with episodes of epidemics of diseases as coffee rust. 

Different natural processes, such as the climate, can have different and recurrent behaviors in time. Despite its periodicity, climate change has impacted on recurring events, both in their temporality and intensity. Thus, climate variables have properties of dynamic deterministic or nonlinear systems. The recurrence analysis of states in these systems is one of the solutions to carry out a study of their behavior in the time-domain.  Eckmann et al. proposed the Recurrence Plots (RP) for the visualization of state recurrence, allowing to see the space phase trajectories in a bidimensional representation. This analysis, initially applied to a single time series and its recurrence with itself, can also be extended to compare two time series by Cross Recurrence Plots (CRP) and find the recurrence between them. Moreover, the elements of PR and CRP can be quantified, obtaining direct elements of comparison between series or pairs of time series.

The aim of this analysis was to find the times and conditions in which the time series of the climatic variables present events related to anomalies or extreme values in the CLRI time series. In addition, the recurrence analysis allows to know the time delay for which each climatic variable affects the disease.


 J. Avelino et al., «The coffee rust crises in Colombia and Central America (2008–2013): impacts, plausible causes and proposed solutions», Food Secur.,  7(2), 303-321, 2015.

 J. M. Waller, M. Bigger, y R. J. Hillocks, Coffee pests, diseases and their management. CABI, 2007.

 A. C. Kushalappa y A. B. Eskes, «Advances in coffee rust research», Annu. Rev. Phytopathol., 27(1), 503–531, 1989.

 E. Lasso, D. C. Corrales, J. Avelino, E. de Melo Virginio Filho, y J. C. Corrales, «Discovering weather periods and crop properties favorable for coffee rust incidence from feature selection approaches», Comput. Electron. Agric., 176, 105640, 2020.

 J. P. Eckmann, S. O. Kamphorst, y D. Ruelle, «Recurrence plots of dynamical systems», World Sci. Ser. Nonlinear Sci. Ser. A, 16, 441–446, 1995.


Technical support of Telematics Engineering Group (GIT) of the University of Cauca, the Tropical Agricultural Research and Higher Education Center (CATIE) and the InnovAccion Cauca project of the Colombian Science, Technology, and Innovation Fund (SGR- CTI) for PhD scholarship granted to MSc. Lasso is acknowledge. Financial support by Fundación Premio Arce (ETSIAAB, UPM) financial support under contract FPA18PPMAT08 is greatly appreciated.

How to cite: Tarquis, A. M., Lasso, E., Corrales, J. C., and de Melo, E.: Analysis of time series recurrence and cross recurrence in the relationship of climate with Coffee Leaf Rust, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16527, https://doi.org/10.5194/egusphere-egu21-16527, 2021.

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