Evidence map›Paper›PMID 36583138›Full record

ArticleEarth's future2022

Crop Yield Loss Risk Is Modulated by Anthropogenic Factors.

Pekka Kinnunen, Matias Heino, Vilma Sandström, Maija Taka, Deepak K Ray, Matti Kummu

Abstract read
In one paragraph

Article in Earth's future, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Pekka KinnunenWater and Development Research Group Aalto University Espoo Finland.ORCID https://orcid.org/0000-0003-0373-2795
Matias HeinoWater and Development Research Group Aalto University Espoo Finland.ORCID https://orcid.org/0000-0003-4294-7756
Vilma SandströmWater and Development Research Group Aalto University Espoo Finland.ORCID https://orcid.org/0000-0002-0475-7487
Maija TakaWater and Development Research Group Aalto University Espoo Finland.ORCID https://orcid.org/0000-0002-6147-9137
Deepak K RayInstitute on the Environment University of Minnesota Twin Cities MN USA.ORCID https://orcid.org/0000-0002-2856-9608
Matti KummuWater and Development Research Group Aalto University Espoo Finland.ORCID https://orcid.org/0000-0001-5096-0163

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

High crop yield variation between years-caused by extreme shocks on the food production system such as extreme weather-can have substantial effects on food production. This in turn introduces vulnerabilities into the global food system. To mitigate the effects of these shocks, there is a clear need to understand how different adaptive capacity measures link to crop yield variability. While existing literature provides many local-scale studies on this linkage, no comprehensive global assessment yet exists. We assessed reported crop yield variation for wheat, maize, soybean, and rice for the time period 1981-2009 by measuring both yield loss risk (variation in negative yield anomalies considering all years) and changes in yields during "dry" shock and "hot" shock years. We used the machine learning algorithm XGBoost to assess the explanatory power of selected gridded indicators of anthropogenic factors globally (i.e., adaptive capacity measures such as the human development index, irrigation infrastructure, and fertilizer use) on yield variation at a 0.5° resolution within climatically similar regions (to rule out the role of average climate conditions). We found that the anthropogenic factors explained 40%-60% of yield loss risk variation across the whole time period, whereas the factors provided noticeably lower (5%-20%) explanatory power during shock years. On a continental scale, especially in Europe and Africa, the factors explained a high proportion of the yield loss risk variation (up to around 80%). Assessing crop production vulnerabilities on global scale provides supporting knowledge to target specific adaptation measures, thus contributing to global food security.

Indexed as

crop yield variabilityfood production shocksfood securitymachine learning

Identifiers

PMID36583138
PMCPMC9786645

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.