The Impact of Climate Change on Agriculture: Nonlinear Effects and Aggregation Bias in Ricardian Models of Farmland Values

Fezzi, Carlo; Bateman, Ian J.

doi:10.1086/680257

Cited by 72 publications

(61 citation statements)

References 43 publications

(33 reference statements)

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“…Inclusion of precipitation has not, for example, substantially improved statistical yield predictions for maize in the US Midwest relative to models based on temperature alone 4,6,8 . It may be that precipitation is a poor proxy for plant-available soil moisture because of variable runoff, drainage and evaporation 9 , as well as the fact that precipitation estimates are generally more uncertain because rainfall is more heterogeneous than temperature variations 10 . Nonlinearities in the relationship between plant-available soil moisture and yield 8,[11][12][13][14][15][16][17][18] could also obscure the influence of moisture availability.…”

mentioning

confidence: 99%

Combined influence of soil moisture and atmospheric evaporative demand is important for accurately predicting US maize yields

Rigden¹,

Mueller²,

Holbrook³

et al. 2020

Nat Food

148

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Understanding the response of agriculture to heat and moisture stress is essential to adapt food systems under climate change. Although evidence of crop yield loss with extreme temperature is abundant, disentangling the roles of temperature and moisture in determining yield has proved challenging, largely due to limited soil moisture data and the tight coupling between moisture and temperature at the land surface. Here, using well-resolved observations of soil moisture from the recently launched Soil Moisture Active Passive satellite, we quantify the contribution of imbalances between atmospheric evaporative demand and soil moisture to maize yield damage in the US Midwest. We show that retrospective yield predictions based on the interactions between atmospheric demand and soil moisture significantly outperform those using temperature and precipitation singly or together. The importance of accounting for this water balance is highlighted by the fact that climate simulations uniformly predict increases in atmospheric demand during the growing season but the trend in root-zone soil moisture varies between models, with some models indicating that yield damages associated with increased evaporative demand are moderated by increased water supply. A damage estimate conditioned only on simulated changes in atmospheric demand, as opposed to also accounting for changes in soil moisture, would erroneously indicate approximately twice the damage. This research demonstrates that more accurate predictions of maize yield can be achieved by using soil moisture data and indicates that accurate estimates of how climate change will influence crop yields require explicitly accounting for variations in water availability.

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mentioning

confidence: 99%

Combined influence of soil moisture and atmospheric evaporative demand is important for accurately predicting US maize yields

Rigden¹,

Mueller²,

Holbrook³

et al. 2020

Nat Food

148

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“…We rely on a log-linear Ricardian model because land values, in Italy as in other countries, are log-normally distributed (Schlenker, Hanemann and Fisher, 2006;Fezzi and Bateman, 2015;Van Passel, Massetti and Mendelsohn, 2016).…”

Section: Methodsmentioning

confidence: 99%

A Ricardian Analysis of the Impact of Climate Change on Italian Agriculture

et al. 2017

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This research investigates the potential impact of warming on Italian agriculture. Using a detailed dataset of 16,000 farms across Italy, the study examines likely warming impacts in different regions and for different sectors of Italian agriculture. The study finds that farm net revenues are very sensitive to seasonal changes in temperature and precipitation. Livestock and crop farms have different responses to climate as do rainfed farms and irrigated farms. The overall results suggest mild consequences from marginal changes in climate but increasingly harmful effects from more severe climate scenarios.

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“…Mendelsohn et al, 1996;Kurukulasuriya and Ajwad, 2007;Fleischer et al, 2008;Seo and Mendelsohn, 2008a;Coster and Adeoti, 2015) as well as inverse and semi-logarithmic functions (e.g. Chatzopoulos and Lippert, 2015;Fezzi and Bateman, 2015). As noted by Fezzi and Bateman (2015), different functional forms for the relationship between revenue and the climatic variables can be encompassed by fitting a semi-parametric model.…”

Section: An Overview Of the Srmmentioning

confidence: 99%

“…Chatzopoulos and Lippert, 2015;Fezzi and Bateman, 2015). As noted by Fezzi and Bateman (2015), different functional forms for the relationship between revenue and the climatic variables can be encompassed by fitting a semi-parametric model. 3 However, such a model is feasible only when the number of observations is sufficiently large to overcome the 'curse of dimensionality'.…”

Section: An Overview Of the Srmmentioning

confidence: 99%

Climate Change, Crop Selection and Agricultural Revenue in Ghana: A Structural Ricardian Analysis

Etwire

Fielding

Kahui

2018

J Agricultural Economics

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We apply a Structural Ricardian Model (SRM) to farm-level data from Ghana for seven principal food crops in order to model the factors which influence farmers' decisions about which food crops to grow and the revenue conditional on these choices. Our application of the SRM incorporates a flexible functional form to allow for the possibility that the effects of temperature and rainfall may not be linearly separable. We use this model as a basis for simulations of the effects of climate change. These simulations suggest that extreme climate change will lead to a significant reduction in the average net revenue per hectare from maize, which accounts for over half of current food production. Across a range of climate change scenarios, there is also substantial substitution of maize for heat-tolerant millet, and a reduction in the cultivation of other crops. Under most of the climate change scenarios that we consider, these results imply a substantial reduction in the aggregate value of agricultural production, since millet is the lowest-value crop.

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The Impact of Climate Change on Agriculture: Nonlinear Effects and Aggregation Bias in Ricardian Models of Farmland Values

Cited by 72 publications

References 43 publications

Combined influence of soil moisture and atmospheric evaporative demand is important for accurately predicting US maize yields

Combined influence of soil moisture and atmospheric evaporative demand is important for accurately predicting US maize yields

A Ricardian Analysis of the Impact of Climate Change on Italian Agriculture

Climate Change, Crop Selection and Agricultural Revenue in Ghana: A Structural Ricardian Analysis

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