Modelling global impacts of climate variability and trend on maize yield during 1980–2010

Yin, Xiaomeng; Leng, Guoyong

doi:10.1002/joc.6792

Cited by 12 publications

(13 citation statements)

References 59 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In general, large‐scale climate impact assessment on crop yield are conducted using statistical (Lobell et al, 2011a; Ray et al, 2015; Schlenker & Roberts, 2009), process‐based (Asseng et al, 2014; Franke et al, 2020; Rosenzweig et al, 2014), or machine learning‐based models (Cao et al, 2021; Kang et al, 2020; Schwalbert et al, 2020). Since different types of prediction approaches have their own strengths and weaknesses, a systematic intercomparison between crop models is needed for robust assessment of agricultural climate impacts (Leng & Hall, 2020; Lobell & Asseng, 2017; Roberts et al, 2017; Yin et al, 2022; Yin & Leng, 2020). Indeed, using multiple types of prediction approaches could provide a better estimate of uncertainty range than a single method, and such information is critical for effective policy‐makings.…”

Section: Introductionmentioning

confidence: 99%

Aggravation of global maize yield loss risk under various hot and dry scenarios using multiple types of prediction approaches

Yin,

Leng,

Huang

et al. 2024

Intl Journal of Climatology

View full text Add to dashboard Cite

High temperature and drought are widely known to cause a reduction of crop yield, but the simultaneously occurring risks in major producing countries and the associated uncertainty across various climate change scenarios remain unclear at the global scale. Here, we evaluate global maize yield loss risk (i.e., the probability of yield reduction by over 10% relative to historical trend yield during 1981–2010) across 30 hot and dry scenarios using regression, machine learning and process‐based models. Besides examining yield loss risk in a single country, we predict the potential risks simultaneously occurring in the top two and top ten producing countries. The three approaches agree on the aggravation of yield loss risk under dry and hot scenarios, but show large discrepancy in the magnitude and sensitivities. Specifically, 2°C warming alone could lead to a global yield loss risk of 73%, 100% and 62% based on regression, long‐short term memory (LSTM) and process‐based models, respectively, and warming‐induced risks can be further aggravated by droughts especially in process models. Global yield loss by over 10% would even become the new norm (i.e., yield loss probability is 100%) when temperature increases by over 2°C in some models. Importantly, the probabilities of yield loss simultaneously occurring in the top two countries (i.e., United States and China) and top ten countries are unexpectedly high and could even become 100% under extreme hot and dry scenarios. Our results highlight the large risks that future climate change may bring to multiple exporting and importing countries simultaneously, thus threating global food market and security. We also emphasize the important value of using different types of prediction approaches for yield projection under hot and dry scenarios, which enables more realistic estimation of uncertainty range than a single type of model.

show abstract

Section: Introductionmentioning

confidence: 99%

Aggravation of global maize yield loss risk under various hot and dry scenarios using multiple types of prediction approaches

Yin,

Leng,

Huang

et al. 2024

Intl Journal of Climatology

View full text Add to dashboard Cite

show abstract

“…Thus, in the subzone of unstable moistening of the left-bank foreststeppe of Ukraine, a higher correlation was observed between climatic indicators and productivity during the critical growing season of corn [7]. Climate change also accounts for 42% of global variations in corn yield [8]. The creation of stable agrocenoses, taking into account the morphometric and productive parameters of the crop, is not possible without the use of an adaptive and diversified system of corn hybrids [9].…”

Section: Introductionmentioning

confidence: 99%

Formation of stable agrocenoses of corn hybrids in a sharply continental climate

Litvinova

Bopp

Галеев

et al. 2022

IOP Conf. Ser.: Earth Environ. Sci.

View full text Add to dashboard Cite

The article considers the factors influencing the stability of agrocenoses of corn hybrids in the forest-steppe zone of the Krasnoyarsk Territory. The researches were carried out for three years in the zone of sharply continental climate of the Krasnoyarsk forest-steppe on leached low- and medium-thick chernozems. The main factors influencing the stability of agrocenoses of corn hybrids were determined. So the factor of climatic conditions affects the corn hybrids, its influence is no more than 14%. The factor of pre-sowing preparation and the precursor (cereals and fallow) showed not significant effect, no more than 2%, on the formation of agrocenoses of corn hybrids. Thus, the factor genotype of the hybrid was fundamental in determining its significance for agrocenoses. The main biometric factor that determines the productivity of corn hybrids was also identified. Correlation analysis made it possible to reveal the influence of the average plant height on the productivity index of corn, as well as on the ear weight. But at the same time, it was determined that the mass of corn ears decreases with an increase in the average height, in the structure of the crop agrocenoses of corn hybrids.

show abstract

“…In an effort to address these challenges, we analysed to what extent the integration of crop models and phenological monitoring can help reduce these design and temporal basis risks, respectively. Biophysical crop simulation models can be leveraged to generate larger synthetic yield datasets, which can then be used to train weather-or satellite-based index models [18][19][20] or support spatial targeting of limited numbers of CCEs that can be conducted as part of area-yield insurance products. However, to date, this approach has not been widely applied in the context of index insurance design, with limited evidence about its performance at spatial scales relevant for insurance applications (e.g., field, farm or village) or in comparison with index models derived empirically from available observational yield datasets.…”

Section: Introductionmentioning

confidence: 99%

Improving the Performance of Index Insurance Using Crop Models and Phenological Monitoring

et al. 2021

View full text Add to dashboard Cite

Extreme weather events cause considerable damage to the livelihoods of smallholder farmers globally. Whilst index insurance can help farmers cope with the financial consequences of extreme weather, a major challenge for index insurance is basis risk, where insurance payouts correlate poorly with actual crop losses. We analyse to what extent the use of crop simulation models and crop phenology monitoring can reduce basis risk in index insurance. Using a biophysical process-based crop model (Agricultural Production System sIMulator (APSIM)) applied for rice producers in Odisha, India, we simulate a synthetic yield dataset to train non-parametric statistical models to predict rice yields as a function of meteorological and phenological conditions. We find that the performance of statistical yield models depends on whether meteorological or phenological conditions are used as predictors and whether one aggregates these predictors by season or crop growth stage. Validating the preferred statistical model with observed yield data, we find that the model explains around 54% of the variance in rice yields at the village cluster (Gram Panchayat) level, outperforming vegetation index-based models that were trained directly on the observed yield data. Our methods and findings can guide efforts to design smart phenology-based index insurance and target yield monitoring resources in smallholder farming environments.

show abstract

Modelling global impacts of climate variability and trend on maize yield during 1980–2010

Cited by 12 publications

References 59 publications

Aggravation of global maize yield loss risk under various hot and dry scenarios using multiple types of prediction approaches

Aggravation of global maize yield loss risk under various hot and dry scenarios using multiple types of prediction approaches

Formation of stable agrocenoses of corn hybrids in a sharply continental climate

Improving the Performance of Index Insurance Using Crop Models and Phenological Monitoring

Contact Info

Product

Resources

About