Identifying the Economic Impacts of Climate Change on Agriculture

Carter, Colin; Cui, Xiaomeng; Ghanem, Dalia; Mérel, Pierre

doi:10.1146/annurev-resource-100517-022938

Cited by 72 publications

(37 citation statements)

References 90 publications

(115 reference statements)

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“…Irshad, Xin, Shahriar, et al (2018) document a similar results between Pakistan and its rice trading partner. Local trade networks and agglomeration patterns are shaped by contiguity, which resultantly structures economic activities (Carter et al, 2018).…”

Section: Resultsmentioning

confidence: 99%

Determinants and Potential of Agri-Food Trade Using the Stochastic Frontier Gravity Model: Empirical Evidence From Nigeria

2021

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Considering the importance of agri-food exports for Nigeria in the face of dwindling revenue from its oil exports. Therefore, this study provides empirical insights on the determinants and potential of agri-food exports from Nigeria to 70 major trading countries between 1995 and 2019 by applying a Stochastic Frontier Analysis (SFA) on a gravity model. We also estimate a variety of techniques, including the fixed effects, Ordinary Least Square (OLS), Pseudo Poisson Maximum Likelihood (PPML), and Heckman models to confirm the robustness of our results. We show that the economic size (GDP) of Nigeria and its trading countries, importers’ population, EU membership, ECOWAS membership and contiguity stimulate agri-food export. Also, we show that bilateral distance, domestic population, exchange rate, language, and landlocked adversely affect agri-food exports. The potential for agri-food trade expansion exists with mostly world biggest economies (including China, the USA, Brazil, India, Russia, Japan, and EU countries) and Nigeria’s border countries. Policy directions for agri-food export expansion are provided.

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Section: Resultsmentioning

confidence: 99%

Determinants and Potential of Agri-Food Trade Using the Stochastic Frontier Gravity Model: Empirical Evidence From Nigeria

2021

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“…Examples include changes in crop varieties, species, planting times, irrigation, as well as more transformational changes such as crop relocation. Other studies have focused more on the economic implications of adaptation [Seo and Mendelsohn, 2008, Carter et al, 2018, Dalhaus et al, 2018. In principle, models can also incorporate a number of adaptation options, such as different crop varieties or species, planting dates and irrigation.…”

Section: Discussionmentioning

confidence: 99%

An empirical, Bayesian approach to modelling crop yield: Maize in USA

Shirley

Pope

Bartlett

et al. 2020

Environ. Res. Commun.

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We apply an empirical, data-driven approach for describing crop yield as a function of monthly temperature and precipitation by employing generative probabilistic models with parameters determined through Bayesian inference. Our approach is applied to state-scale maize yield and meteorological data for the US Corn Belt from 1981 to 2014 as an exemplar, but would be readily transferable to other crops, locations and spatial scales. Experimentation with a number of models shows that maize growth rates can be characterised by a two-dimensional Gaussian function of temperature and precipitation with monthly contributions accumulated over the growing period. This approach accounts for non-linear growth responses to the individual meteorological variables, and allows for interactions between them. Our models correctly identify that temperature and precipitation have the largest impact on yield in the six months prior to the harvest, in agreement with the typical growing season for US maize (April to September). Maximal growth rates occur for monthly mean temperature 18°C-19°C, corresponding to a daily maximum temperature of 24°C-25°C (in broad agreement with previous work) and monthly total precipitation 115 mm. Our approach also provides a self-consistent way of investigating climate change impacts on current US maize varieties in the absence of adaptation measures. Keeping precipitation and growing area fixed, a temperature increase of 2°C, relative to 1981-2014, results in the mean yield decreasing by 8%, while the yield variance increases by a factor of around 3. We thus provide a flexible, data-driven framework for exploring the impacts of natural climate variability and climate change on globally significant crops based on their observed behaviour. In concert with other approaches, this can help inform the development of adaptation strategies that will ensure food security under a changing climate.

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“…Cooper et al (2017) studied specification bias in crop yield response function and argued the necessity of a flexible function form. Carter et al (2018) reviewed the literature adopting binned weather variables as regressors and suggested the possibility of multicollinearity when using it in panel regression. Even though there has not been any consensus about picking the "best" specification of heat units, it is agreed that heat should enter the crop yield response function regression nonlinearly.…”

Section: Specification Of Heat Exposure Binsmentioning

confidence: 99%

Spatial Panel Models of Crop Yield Response to Weather: Econometric Specification Strategies and Prediction Performance

Yun

Gramig

2021

J. Agric. Appl. Econ.

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This study scrutinizes spatial econometric models and specifications of crop yield response functions to provide a robust evaluation of empirical alternatives available to researchers. We specify 14 competing panel regression models of crop yield response to weather and site characteristics. Using county corn yields in the US, this study implements in-sample, out-of-sample, and bootstrapped out-of-sample prediction performance comparisons. Descriptive propositions and empirical results demonstrate the importance of spatial correlation and empirically support the fixed effects model with spatially dependent error structures. This study also emphasizes the importance of extensive model specification testing and evaluation of selection criteria for prediction.

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Identifying the Economic Impacts of Climate Change on Agriculture

Cited by 72 publications

References 90 publications

Determinants and Potential of Agri-Food Trade Using the Stochastic Frontier Gravity Model: Empirical Evidence From Nigeria

Determinants and Potential of Agri-Food Trade Using the Stochastic Frontier Gravity Model: Empirical Evidence From Nigeria

An empirical, Bayesian approach to modelling crop yield: Maize in USA

Spatial Panel Models of Crop Yield Response to Weather: Econometric Specification Strategies and Prediction Performance

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