Drivers of crop impacts from tephra fallout

Ligot, Noa; Guevara, Alicia; Delmelle, Pierre

doi:10.30909/vol.05.01.163181

Cited by 10 publications

(28 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Direct negative impacts on and the ability of vegetation to recover from eruptions depend on complex interactions between biotic and abiotic parameters (Ayris and Delmelle, 2012;Arnalds, 2013). Biotic parameters include the type and composition of the vegetation, the biological legacy related to previous stresses and the phenological state of the plant at the time of eruption (Jenkins et al, 2014;Ligot et al, 2022). Abiotic parameters include climate (e.g.…”

Section: Impact Of Volcanic Hazards On Vegetationmentioning

confidence: 99%

“…8a-e) identify similar tephra thresholds as those in existing DDSs (Table 1). Nevertheless, numerous evidences reported in post-EIAs as well as controlled experiments outline the dependency of impact mechanisms to size distribution, ranging from physical impact for large lapilli to a reduction of light interception from fine ash leading to a decrease in photosynthesis (e.g., Ligot et al, 2022). DDSs must therefore consider other hazard impact metrics than only tephra thickness, and Figs.…”

Section: Validation and Causal Inferencementioning

confidence: 99%

“…Note that although pastoral crops are included in the herbaceous vegetation class in CGLS-LC100, it is impossible to distinguish between natural and managed grassland (Buchhorn et al, 2020). Post-EIAs on agricultural impacts have demonstrated how agriculture vulnerability depends on various factors that are not included in our model, including some of socio-economic nature (Blake et al, 2015;Ligot et al, 2022;Magill et al, 2013;Phillips et al, 2019;Wilson et al, 2013Wilson et al, , 2007 that reflect specific challenges associated with different farming activities (e.g., pastoral versus horticultural, intensive versus subsistence farming). Although future evolutions of the CGLS-LC100 dataset will possibly include finer sub-definitions of the crops class (e.g., irrigated versus rainfed cropland, farm size; Buchhorn et al, 2020), the methodology currently considers all agricultural crops as a uniform system.…”

Section: Towards a Model For Agricultural Crops And Food Productionmentioning

confidence: 99%

“…This is especially true for volcanic risk. Our current knowledge of the vulnerability of agriculture to volcanic hazards comes from a combination of opportunistic field-based post-event impact assessments (post-EIAs; e.g., Blake et al, 2015;Le Pennec et al, 2012;Magill et al, 2013;Phillips et al, 2019;Stewart et al, 2016;Wilson et al, 2011a, b;Wilson et al, 2013) and rarer experimental studies (Hotes et al, 2004;Zobel et al, 2022;Ligot et al, 2022). However, the generalization of these empirical lessons is limited by two main aspects.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Insights into the vulnerability of vegetation to tephra fallouts from interpretable machine learning and big Earth observation data

Biass

Jenkins²,

Aeberhard³

et al. 2022

Nat. Hazards Earth Syst. Sci.

Self Cite

View full text Add to dashboard Cite

Abstract. Although the generally high fertility of volcanic soils is often seen as an opportunity, short-term consequences of eruptions on natural and cultivated vegetation are likely to be negative. The empirical knowledge obtained from post-event impact assessments provides crucial insights into the range of parameters controlling impact and recovery of vegetation, but their limited coverage in time and space offers a limited sample of all possible eruptive and environmental conditions. Consequently, vegetation vulnerability remains largely unconstrained, thus impeding quantitative risk analyses. Here, we explore how cloud-based big Earth observation data, remote sensing and interpretable machine learning (ML) can provide a large-scale alternative to identify the nature of, and infer relationships between, drivers controlling vegetation impact and recovery. We present a methodology developed using Google Earth Engine to systematically revisit the impact of past eruptions and constrain critical hazard and vulnerability parameters. Its application to the impact associated with the tephra fallout from the 2011 eruption of Cordón Caulle volcano (Chile) reveals its ability to capture different impact states as a function of hazard and environmental parameters and highlights feedbacks and thresholds controlling impact and recovery of both natural and cultivated vegetation. We therefore conclude that big Earth observation (EO) data and machine learning complement existing impact datasets and open the way to a new type of dynamic and large-scale vulnerability models.

show abstract

Section: Impact Of Volcanic Hazards On Vegetationmentioning

confidence: 99%

Section: Validation and Causal Inferencementioning

confidence: 99%

Section: Towards a Model For Agricultural Crops And Food Productionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Insights into the vulnerability of vegetation to tephra fallouts from interpretable machine learning and big Earth observation data

Biass

Jenkins²,

Aeberhard³

et al. 2022

Nat. Hazards Earth Syst. Sci.

Self Cite

View full text Add to dashboard Cite

show abstract

“…The livelihood and food security of hundreds of millions of people living near and on volcanoes 39 intricately depend on agriculture (Small and Naumann, 2001;Brown et al, 2015). However, farming activities in these regions is exposed to short-term, negative impacts of volcanic eruptions, an issue amplified by the expanding population living under volcanic risk (Brown et (Magill et al, 2013;Ligot et al, 2022). In these areas, the capacity of ash fall to initiate damage to crops hinges on the percentage of leaf surfaces covered by ash, here referred to as ash 87 retention.…”

Section: Introductionmentioning

confidence: 99%

Grain size modulates volcanic ash retention on crop foliage and potential yield loss

Ligot

Bogaert

Biass

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

Abstract. Ash fall from volcanic eruptions endangers crop production and food security and jeopardises agricultural livelihoods. As population in the vicinity of volcanoes continues to grow, strategies to reduce volcanic risks to and impacts on crops are increasingly needed. This effort involves the use of quantitative relationships for anticipating crop damage from ash exposure. However, current limited models of crop vulnerability to ash rely solely on ash thickness (or loading) and fail to reproduce the complex interplay of other volcanic and non-volcanic factors that drive impact. Amongst these, ash retention on crop leaves affects photosynthesis and is ultimately responsible for widespread damage to crops. In this context, we carried out greenhouse experiments to assess how ash grain size, leaf pubescence and humidity conditions at leaf surfaces influence the retention of ash (defined as the percentage of foliar cover coated with ash) in tomato and chilli pepper plants, two crop types commonly grown in volcanic regions. For a fixed ash mass load (~570 g m-2), we found that ash retention decreases exponentially with increasing grain size and is enhanced when leaves are pubescent (such as in tomato) or their surfaces are wet. Assuming that leaf area index (LAI) diminishes with ash retention in tomato and chilli pepper, we derived a new expression for predicting potential crop yield loss after an ash fall event. A corollary result is that the measurement of crop LAI in ash-affected areas may serve as a useful impact metric. Our study demonstrates that quantitative insights into crop vulnerability can be gained rapidly from controlled experiments, thereby providing a mean to improve models that can predict ash risks to crops accurately. We advocate this approach to broaden our understanding of ash-plant interaction and to validate the use of remote sensing methods for assessing crop damage and recovery at various spatial and time scales after an eruption.

show abstract

Exposure of vegetables to simulated volcanic ashfall reveals production loss controlled by plant traits and growth stage

Ligot,

de Tornaco,

Pereira

et al. 2024

Agrosystems Geosci & Env

View full text Add to dashboard Cite

Explosive volcanic eruptions represent a serious threat to agriculture in many countries. Ashfall can cause substantial damage to crops, jeopardizing farmers' livelihoods and potentially endangering food security. Previous field‐based studies have associated ash impact on crops with the deposit thickness, or, correspondingly, with the mass load. However, non‐volcanic factors, including plant traits and growth stage, also influence the vulnerability of crops to ashfall. To accurately estimate the risk of crop production loss in ash‐prone areas, it is essential to evaluate how these factors govern the impact of ash on crops. We grew leafy (lettuce, Lactuca sativa; hative d'Heverlée, and cabbage, Brassica oleracea; cabus de Chateaurenard) and bulb and root (onion, Allium cepa; blanc premier, and carrot, Daucus carota; hative d‘Oxhella) vegetables in a greenhouse and exposed them at two growth stages to simulated ash deposits ranging from 5 to 40 kg m−2. Our results confirm that crop production loss increases with higher ash mass load, reaching 27%–69% for deposits of 20–40 kg m−2. Additionally, they indicate a higher vulnerability of carrot and onion plants than previously reported. Lettuce and cabbage plants were more severely impacted by ash compared to onion and carrot plants, illustrating the role of plant traits in controlling ash interception and retention on foliage. Furthermore, the plant growth stage emerged as another vulnerability factor. Using the new impact data, we calculated a theoretical production loss in a cultivated area potentially affected by ashfall. This revealed that a significant portion of the crop production loss can be associated with low ash mass loads (5 kg m−2), emphasizing the importance of including distal regions in the impact assessment of ashfall on crops.

show abstract

Drivers of crop impacts from tephra fallout

Cited by 10 publications

References 56 publications

Insights into the vulnerability of vegetation to tephra fallouts from interpretable machine learning and big Earth observation data

Insights into the vulnerability of vegetation to tephra fallouts from interpretable machine learning and big Earth observation data

Grain size modulates volcanic ash retention on crop foliage and potential yield loss

Exposure of vegetables to simulated volcanic ashfall reveals production loss controlled by plant traits and growth stage

Contact Info

Product

Resources

About