Modeling the effect of a heat wave on maize production in the USA and its implications on food security in the developing world

Chung, Uran; Gbegbelegbe, Sika; Shiferaw, Bekele; Robertson, Richard D.; Yun, Jin I.; Tesfaye, Kindie; Hoogenboom, Gerrit; Sonder, Kai

doi:10.1016/j.wace.2014.07.002

Cited by 50 publications

(35 citation statements)

References 17 publications

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“…Remote and proximal sensing methodologies are playing an increasingly important role in the agriculture sector [49,50], and their application to the topic of EWE will need to be enlarged. Furthermore, as already stressed by other authors [51][52][53] this manuscript showed that there is a serious concern for food security and economic losses due to EWE. For this reason, future research will have to cope with mitigation measures and governance systems, addressing attention especially towards developing countries.…”

Section: Discussionsupporting

confidence: 59%

Extreme Weather Events in Agriculture: A Systematic Review

et al. 2019

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Despite the increase of publications focusing on the consequences of extreme weather events (EWE) for the agricultural sector, a specific review of EWE related to agriculture is missing. This work aimed at assessing the interrelation between EWE and agriculture through a systematic quantitative review of current scientific literature. The review analysed 19 major cropping systems (cereals, legumes, viticulture, horticulture and pastures) across five continents. Documents were extracted from the Scopus database and examined with a text mining tool to appraise the trend of publications across the years, the specific EWE-related issues examined and the research gaps addressed. The results highlighted that food security and economic losses due to the EWE represent a major interest of the scientific community. Implementation of remote sensing and imagery techniques for monitoring and detecting the effects of EWE is still underdeveloped. Large research gaps still lie in the areas concerning the effects of EWE on major cash crops (grapevine and tomato) and the agronomic dynamics of EWE in developing countries. Current knowledge on the physiological dynamics regulating the responses of main crops to EWE appears to be well established, while more research is urgently needed in the fields of mitigation measures and governance systems.

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Section: Discussionsupporting

confidence: 59%

Extreme Weather Events in Agriculture: A Systematic Review

et al. 2019

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“…These fluctuations, or yield variability (YV), are undesirable, since they undermine food security on three dimensions (Morton 2007, Schmidhuber and Tubiello 2007, Wheeler and von Braun 2013, Thornton et al 2014. First, the amount of harvested food can be lower than necessary, second, the financial sustainability of farming systems can be challenged, and third, the access to nutritious food can be diminished by rising prices or export bans connected to variable yields (Headey and Fan 2008, Headey 2010, Coumou and Rahmstorf 2012, Chung et al 2014. Substantial fractions of historic YV can be explained by weather variability and extremes like droughts, floods, heat waves, cold spells, or combinations of them (Porter and Semenov 2005, Schlenker and Roberts 2009, Coumou and Rahmstorf 2012, Lobell et al 2013, Deryng et al 2014, Ray et al 2015, Lesk et al 2016.…”

Section: Introductionmentioning

confidence: 99%

A network-based approach for semi-quantitative knowledge mining and its application to yield variability

Schauberger

Rolinski

Müller

2016

Environ. Res. Lett.

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Variability of crop yields is detrimental for food security. Under climate change its amplitude is likely to increase, thus it is essential to understand the underlying causes and mechanisms. Crop models are the primary tool to project future changes in crop yields under climate change. A systematic overview of drivers and mechanisms of crop yield variability (YV) can thus inform crop model development and facilitate improved understanding of climate change impacts on crop yields. Yet there is a vast body of literature on crop physiology and YV, which makes a prioritization of mechanisms for implementation in models challenging. Therefore this paper takes on a novel approach to systematically mine and organize existing knowledge from the literature. The aim is to identify important mechanisms lacking in models, which can help to set priorities in model improvement. We structure knowledge from the literature in a semi-quantitative network. This network consists of complex interactions between growing conditions, plant physiology and crop yield. We utilize the resulting network structure to assign relative importance to causes of YV and related plant physiological processes. As expected, our findings confirm existing knowledge, in particular on the dominant role of temperature and precipitation, but also highlight other important drivers of YV. More importantly, our method allows for identifying the relevant physiological processes that transmit variability in growing conditions to variability in yield. We can identify explicit targets for the improvement of crop models. The network can additionally guide model development by outlining complex interactions between processes and by easily retrieving quantitative information for each of the 350 interactions. We show the validity of our network method as a structured, consistent and scalable dictionary of literature. The method can easily be applied to many other research fields.

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“…Under climate change episodes of high temperature are expected to increase in frequency and duration. This could threaten regional productivity in already susceptible areas1234. There are a number of statistical approaches that allow for separating effects of high temperatures on observed yields from other sources of variability that are not correlated with them over time.…”

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confidence: 99%

Consistent negative response of US crops to high temperatures in observations and crop models

et al. 2017

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High temperatures are detrimental to crop yields and could lead to global warming-driven reductions in agricultural productivity. To assess future threats, the majority of studies used process-based crop models, but their ability to represent effects of high temperature has been questioned. Here we show that an ensemble of nine crop models reproduces the observed average temperature responses of US maize, soybean and wheat yields. Each day >30 °C diminishes maize and soybean yields by up to 6% under rainfed conditions. Declines observed in irrigated areas, or simulated assuming full irrigation, are weak. This supports the hypothesis that water stress induced by high temperatures causes the decline. For wheat a negative response to high temperature is neither observed nor simulated under historical conditions, since critical temperatures are rarely exceeded during the growing season. In the future, yields are modelled to decline for all three crops at temperatures >30 °C. Elevated CO2 can only weakly reduce these yield losses, in contrast to irrigation.

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Modeling the effect of a heat wave on maize production in the USA and its implications on food security in the developing world

Abstract: Journal articleIFPRI3; CRP2PIMPRCGIAR Research Program on Policies, Institutions, and Markets (PIM

Cited by 50 publications

References 17 publications

Extreme Weather Events in Agriculture: A Systematic Review

Extreme Weather Events in Agriculture: A Systematic Review

A network-based approach for semi-quantitative knowledge mining and its application to yield variability

Consistent negative response of US crops to high temperatures in observations and crop models

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