Weather Extremes That Affect Various Agricultural Commodities

Grotjahn, Richard

doi:10.1002/9781119413738.ch3

Cited by 9 publications

(6 citation statements)

References 126 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Regions whose climate change responses feature peak or dip characteristics feature substantively non‐linear responses that alter the sign of productivity change trend at higher GWLs. While the causes of this shift require further analysis for any given region, this reversal is often the result of a tipping‐point phenomena, such as: (a) the emergence of a new agroclimatic hazard (negative inflection; e.g., elevated probability of drought at higher GWL; Seneviratne et al., 2021); (b) a consistent trend causing conditions to surpass a biophysical tolerance level (negative inflection; e.g., extreme temperatures surpassing critical limits; Grotjahn, 2021); (c) a boon condition overwhelming competing adverse hazards (positive inflection; e.g., the beneficial effects of CO 2 for growth and water retention; Toreti et al., 2020); or (d) a positive response having diminishing benefits as GWL increases continue (negative inflection; e.g., CO 2 benefits per ppm are reduced at higher concentration levels; Franke et al., 2019). Regions where wheat or rice crops are grown in multiple seasons may also show a reversal when productivity changes in one season surpass changes in the other.…”

Section: Climate Change Response Patternsmentioning

confidence: 99%

Non‐Linear Climate Change Impacts on Crop Yields May Mislead Stakeholders

Ruane,

Phillips,

Jägermeyr

et al. 2024

Earth's Future

View full text Add to dashboard Cite

We utilize a global warming level (GWL) lens to evaluate global and regional patterns of agricultural impacts as global surface temperature increases, providing a unique perspective on the experience of stakeholders with continued warming in the 21st century. We analyze crop productivity outputs from 11 crop models simulating 5 climate models under 3 emissions scenarios across 4 crops within the AgMIP/ISIMIP Phase 3 ensemble. We categorize regional productivity changes (without adaptation) into 9 characteristic climate change response patterns, identifying consistent increases and decreases as well as non‐linear (peak or dip) responses indicative of inflection points reversing trends as GWLs increase. Many maize regions and pockets of wheat, rice and soybean show peak decrease patterns where initial increases may lull stakeholders into complacency or maladaptation before productivity shifts to losses at higher GWLs. Although the GWL perspective has proven useful in connecting diverse climate models and emissions scenarios, we identify multiple pitfalls that recommend proceeding with caution when applying this approach to climate impacts. Chief among these is that carbon dioxide (CO2) concentrations at any GWL depend on a climate model's transient climate response (TCR). Higher CO2 concentrations generally benefit crop productivity, so this leads to more pessimistic agricultural projections for so‐called “hot” models and can skew multi‐model ensemble results as models with high TCR are disproportionately likely to reach higher GWLs. While there are strong connections between many climatic impact‐drivers and GWLs, vulnerability and exposure components of food system risk are strongly dependent on development pathways.

show abstract

Section: Climate Change Response Patternsmentioning

confidence: 99%

Non‐Linear Climate Change Impacts on Crop Yields May Mislead Stakeholders

Ruane,

Phillips,

Jägermeyr

et al. 2024

Earth's Future

View full text Add to dashboard Cite

show abstract

“…Although heatwaves represented around 2% of the weather-and climate-related disasters recorded worldwide from 1970 to 2019, they have been accountable for 8% of disasters' caused deaths [9]. In addition to higher rates of thermal stress and mortality [10][11][12], high temperatures and prolonged hot weather are related to reductions in productivity [13,14], substantial agricultural losses [15,16], wildfires, damage to transport infrastructure, power failures, and sharp jumps in water and energy consumption [17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Spatio-Temporal Variation of Extreme Heat Events in Southeastern Europe

2022

View full text Add to dashboard Cite

Many studies in the last few years have been dedicated to the increasing temperatures and extreme heat in Europe since the second half of the 20th century because of their adverse effects on ecosystems resilience, human health, and quality of life. The present research aims to analyze the spatio-temporal variations of extreme heat events in Southeastern Europe using daily temperature data from 70 selected meteorological stations and applying methodology developed initially for the quantitative assessment of hot weather in Bulgaria. We demonstrate the suitability of indicators based on maximum temperature thresholds to assess the intensity (i.e., magnitude and duration) and the tendency of extreme heat events in the period 1961–2020 both by individual stations and the Köppen’s climate zones. The capability of the used intensity-duration hot spell model to evaluate the severity of extreme heat events has also been studied and compared with the Excess Heat Factor severity index on a yearly basis. The study provides strong evidence of the suitability of the applied combined approach in the investigation of the spatio-temporal evolution of the hot weather phenomena over the considered domain.

show abstract

“…In maize plants, hail damage can interfere with the movement of assimilates in the plant (Dungan, 1934). Moreover, damage by hail can trigger pathogens attacks and leaves losses, further reducing the loss of photosynthetically active areas and, with this, the reduction in grain yield (Johnson, 1978;Grotjahn, 2021) and the amount of biomass collected (Furlanetto et al, 2021).…”

Section: Introduction Maize Cultivation In Italymentioning

confidence: 99%

Assessment of hail damages in maize using remote sensing and comparison with an insurance assessment: A case study in Lombardy

Schillaci¹,

Inverardi²,

Battaglia³

et al. 2022

Ital J Agronomy

View full text Add to dashboard Cite

Studies have shown that the quantification of hail damage is generally inaccurate and is influenced by the experience of the field surveyors/technicians. To overcome this problem, the vegetation indices retrieved by remote sensing, can be used to get information about the hail damage. The aim of this work is the detection of medium-low damages (i.e., between 10 and 30% of the gross saleable production) using the much-used normalized difference vegetation index (NDVI) in comparison with alternative vegetation indices (i.e., ARVI, MCARI, SAVI, MSAVI, MSAVI2) and their change from pre-event to post-event in five hailstorms in Lombardy in 2018. Seventy-four overlapping scenes (10% cloud cover) were collected from the Sentinel-2 in the spring-summer period of 2018 in the Brescia district (Lombardy). An unsupervised classification was carried out to automatically identify the maize fields (grain and silage), testing the change detection approach by searching for damage by hail and strong wind in the Lombardy plain of Brescia. A database of 125 field surveys (average size 4 Ha) after the hailstorm collected from the insurance service allowed for the selection of the dates on which the event occurred and provided a proxy of the extent of the damage (in % of the decrease of the yield). Hail and strong wind damages ranged from 5 to 70%, and they were used for comparison with the satellite image change detection. The differences in the vegetation indices obtained by Sentinel 2 before and after the hailstorm and the insurance assessments of damage after the events were compared to assess the degree of concordance. The modified soil-adjusted vegetation index outperformed other vegetation indices in detecting hail-related damages with the highest accuracy (73.3%). On the other hand, the NDVI resulted in scarce performance ranking last of the six indices, with an accuracy of 65.3%. Future research will evaluate how much uncertainty can be found in the method’s limitations with vegetation indices derived from satellites, how much is due to errors in estimating damage to the ground, and how much is due to other causes. Highlights - The discovery rate of damaged fields improved. - MSAVI outperformed NDVI and other vegetation indices, identifying 73.3% of occurrences. - Estimation of damage from remote sensing was more accurate for fields severely affected >50%. - In low-intensity hail events (<50 canopies affected), the MSAVI provided a detailed picture of the damage across the field. - The proposed approach is promising to develop a ‘sampling map’ for detailed on-ground assessment.

show abstract

Weather Extremes That Affect Various Agricultural Commodities

Cited by 9 publications

References 126 publications

Non‐Linear Climate Change Impacts on Crop Yields May Mislead Stakeholders

Non‐Linear Climate Change Impacts on Crop Yields May Mislead Stakeholders

Spatio-Temporal Variation of Extreme Heat Events in Southeastern Europe

Assessment of hail damages in maize using remote sensing and comparison with an insurance assessment: A case study in Lombardy

Contact Info

Product

Resources

About