The Use of Panel Models in Assessments of Climate Impacts on           Agriculture

Blanc, Élodie; Schlenker, Wolfram

doi:10.1093/reep/rex016

Cited by 140 publications

(98 citation statements)

References 31 publications

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“…As a second robustness test, I add a series of time-varying controls to my aggregate yield regression (Appendix Table A3). The inclusion of time-varying controls complements the balance test in Section 4.3 and provides another way to allay concerns about potential omitted variable bias (Blanc and Schlenker, 2017). The first column presents the baseline results, with no added controls.…”

Section: Robustnessmentioning

confidence: 99%

Can farmers adapt to higher temperatures? Evidence from India

Taraz

2018

World Development

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Projections suggest that the damages from climate change will be substantial for developing countries. Understanding the ability of households in these countries to adapt to climate change is critical in order to determine the magnitude of the potential damages. In this paper, I investigate the ability of farmers in India to adapt to higher temperatures. I use a methodology that exploits short-term weather fluctuations as well as spatial variation in long-run climate. Specifically, I estimate how damaging high temperatures are for districts that experience high temperatures more or less frequently. I find that the losses from high temperatures are lower in heat-prone districts, a result that is consistent with adaptation. However, while adaptation appears to be modestly effective for moderate levels of heat, my results suggest that adaptation to extreme heat is much more difficult. Extremely high temperatures do grave damage to crops, even in places that experience these temperature extremes regularly. The persistence of negative impacts of high temperatures, even in areas that experience high temperatures frequently, underscores the need for development policies that emphasize risk mitigation and explicitly account for climate-change-related risks.JEL Classification: O1, O3, Q1, Q5

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Section: Robustnessmentioning

confidence: 99%

Can farmers adapt to higher temperatures? Evidence from India

Taraz

2018

World Development

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“…In addition, using a fixed-effect model is equivalent to take the deviation from the respective mean of each considered variable. This is relevant for weather variables because the deviations from their respective means, i.e., weather shocks, tend to be random, and therefore exogenous with respect to the choice of farm inputs [32,33]. Hence, the omission of these inputs will not affect the estimated parameters on weather variables.…”

Section: Panel Data Modelsmentioning

confidence: 99%

Climate Change, Agriculture and Migration: A Survey

2018

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This paper proposes a selective review of the classical economics-based literature on climate change and migration, focusing on the extent to which agriculture might be considered a key mediating channel linking climate change to migration. Overall, climate change is expected to have large and negative effects on the global economy. These effects are even more evident whenever the economic sector considered is the agricultural one, particularly in developing countries. Hence, migration can be viewed as a specific form of adaptation implemented by individuals and households, enabling them to cope, among other things, with weather-induced risk. We show that the importance of agriculture emerges from both plenty of micro-level country studies and relatively few macro-level analyses using cross-sectional data over longer time periods. Thus, policy actions targeted to sustainable agriculture and rural development can both help tackle the challenges posed by climate change and create opportunities in the face of growing migration issues. However, we also stress that much of the current evidence is based on statistical associations that have nothing to do with causal inferences. This calls for the use of a more structural approach and more sophisticated research designs, enabling the researchers to better discriminate among different mechanisms concurrently at work. In addition, further research should be addressed to the role played by food security, a complex dimension largely missing in the current debates on climate change and migration.

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“…However, the statistical data of Food and Agriculture Organization of the United Nation (FAO) showed that global wheat yield did not reduce with climate warming in the premise of stable planting areas (http:// www.fao.org/faostat/zh/#data/QC/visualize), which indicated that with the development of agricultural technology, agricultural production is not only related to meteorological factors such as temperature and precipitation but also to economic factors such as labor and fertilizer. erefore, researchers combined meteorological factors and economic factors as independent variables to establish regression models so as to explore the impacts of climate change on agricultural production [24][25][26][27]. Chou et al [28,29] developed a new model (C-D-C) for assessing and predicting the effect of climate change on grain yield by introducing climatic factors into the C-D (Cobb-Dauglas) production function, and the preliminary simulation and verification of the model were performed.…”

Section: Introductionmentioning

confidence: 99%

Climate Change Impacts on Winter Wheat Yield in Northern China

Geng

Fang

Ren

et al. 2019

Advances in Meteorology

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Exploring the impacts of climate change on agriculture is one of important topics with respect to climate change. We quantitatively examined the impacts of climate change on winter wheat yield in Northern China using the Cobb-Douglas production function. Utilizing time-series data of agricultural production and meteorological observations from 1981 to 2016, the impacts of climatic factors on wheat production were assessed. It was found that the contribution of climatic factors to winter wheat yield per unit area (WYPA) was 0.762-1.921% in absolute terms. Growing season average temperature (GSAT) had a negative impact on WYPA for the period of 1981-2016. A 1% increase in GSAT could lead to a loss of 0.109% of WYPA when the other factors were constant. While growing season precipitation (GSP) had a positive impact on WYPA, as a 1% increase in GSP could result in 0.186% increase in WYPA, other factors kept constant. en, the impacts on WYPA for the period 2021-2050 under two different emissions scenarios RCP4.5 and RCP8.5 were forecasted. For the whole study area, GSAT is projected to increase 1.37°C under RCP4.5 and 1.54°C under RCP8.5 for the period 2021-2050, which will lower the average WYPA by 1.75% and 1.97%, respectively. GSP is tended to increase by 17.31% under RCP4.5 and 22.22% under RCP8.5 and will give a rise of 3.22% and 4.13% in WYPA. e comprehensive effect of GSAT and GSP will increase WYPA by 1.47% under RCP4.5 and 2.16% under RCP8.5.

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The Use of Panel Models in Assessments of Climate Impacts on Agriculture

Cited by 140 publications

References 31 publications

Can farmers adapt to higher temperatures? Evidence from India

Can farmers adapt to higher temperatures? Evidence from India

Climate Change, Agriculture and Migration: A Survey

Climate Change Impacts on Winter Wheat Yield in Northern China

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