The Ricardian approach is a popular hedonic method for analyzing climate change impacts on agriculture. The approach typically relies on a cross‐sectional regression of farmland asset prices on fixed ...climate variables, making it particularly vulnerable to omitted variables. I conduct a long‐spanning Ricardian analysis of farmland prices in the eastern United States (1950–2012) and find a convergence of evidence indicating that large estimates of climate change damages for recent cross‐sections (>1970s), also found in the literature, can be explained by the growing influence of omitted factors extraneous to the agricultural sector. I propose and evaluate a simple strategy to circumvent such nonfarm influences in the form of a Ricardian model based on cash rents (2009–2016), which better reflect agricultural profitability and do not capitalize expected land use changes. The new damage estimates on nonirrigated cropland and pasture rents are more optimistic and cannot be distinguished from zero. However, estimates remain imprecise under extreme climate change scenarios pointing to a cautionary long‐term outlook for United States agriculture. The findings are robust to multiple checks and alternative explanations.
Globally, over 400 million tons of biomass are burned in agricultural fires for management purposes each year, substantially affecting air quality (Korontzi et al., Global Biogeochemical Cycles 2006; ...20; Yevich & Logan, Global Biogeochemical Cycles 2003; 17). Rangel and Vogl (Review of Economics and Statistics 2019; 101:616–630) provide evidence that agricultural fires in Brazil cause large declines in newborn health in downwind communities. We replicate this analysis and evaluate the sensitivity of their results to changes in the dataset and alternative modeling choices. Although accounting for the potential of spatial correlation in errors reduces precision of estimated effects, we find that their primary conclusions are robust to alternative specifications and reasonable placebo tests. We discuss how our findings may guide future research on the relationship between agricultural fires and health.
This study illustrates and quantifies how overlooking the impact of weather shocks can affect the measurement and decomposition of agricultural total factor productivity (TFP) change. The underlying ...technology is represented by a flexible input distance function with quasi‐fixed inputs estimated with Bayesian methods. Using agricultural production and weather data for 16 states in the Pacific Region, Central Region, and Southern Plains of the United States, we estimate TFP change as the direct sum of multiple components, including a net weather effect. To assess the role of weather, we conduct a comparative analysis based on two distinct sets of input and output variables. A traditional set of variables that ignore weather variations, and a new set of “weather‐filtered” variables that represent input and output levels that would have been chosen under average weather conditions. From this comparative analysis, we derive biases in the decomposition of TFP growth from the omission of weather shocks. We find that weather shocks accelerated productivity growth in 12 out of 16 states by the equivalent of 11.4% of their group‐average TFP growth, but slowed down productivity by the equivalent of 6.5% of the group‐average TFP growth in the other four states (located in the Northern‐most part of the country). We also find substantial biases in the estimated contribution of technical change, scale effects, technical efficiency change, and output allocation effects to TFP growth (varying in magnitude and direction across regions) when weather effects are excluded from the model. This is the first study to present estimates of those biases based on a counterfactual analysis. One major implication from our study is that the official USDA's measures of TFP change would appear to overestimate the rate of productivity growth in U.S. agriculture stemming from technical change, market forces, agricultural policies, and other nonweather drivers.
Temperature Shocks and Establishment Sales Addoum, Jawad M.; Ng, David T.; Ortiz-Bobea, Ariel
The Review of financial studies,
03/2020, Letnik:
33, Številka:
3
Journal Article
Recenzirano
Combining granular daily data on temperatures across the continental United States with detailed establishment data from 1990 to 2015, we study the causal impact of temperature shocks on ...establishment sales and productivity. Using a large sample yielding precise estimates, we do not find evidence that temperature exposures significantly affect establishment-level sales or productivity, including among industries traditionally classified as “heat sensitive.” At the firm level, we find that temperature exposures aggregated across firm establishments are generally unrelated to sales, productivity, and profitability. Our results support existing findings of a tenuous relation between temperature and aggregate economic growth in rich countries.
In several world regions, climate change is predicted to negatively affect crop productivity. The recent statistical yield literature emphasizes the importance of flexibly accounting for the ...distribution of growing-season temperature to better represent the effects of warming on crop yields. We estimate a flexible statistical yield model using a long panel from France to investigate the impacts of temperature and precipitation changes on wheat and barley yields. Winter varieties appear sensitive to extreme cold after planting. All yields respond negatively to an increase in spring-summer temperatures and are a decreasing function of precipitation about historical precipitation levels. Crop yields are predicted to be negatively affected by climate change under a wide range of climate models and emissions scenarios. Under warming scenario RCP8.5 and holding growing areas and technology constant, our model ensemble predicts a 21.0% decline in winter wheat yield, a 17.3% decline in winter barley yield, and a 33.6% decline in spring barley yield by the end of the century. Uncertainty from climate projections dominates uncertainty from the statistical model. Finally, our model predicts that continuing technology trends would counterbalance most of the effects of climate change.
Predictions of future food supply under climate change rely on projected crop yield trends, which are typically based upon retrospective empirical analyses of historical yield gains. However, the ...estimation of these trends is difficult given the evolving impact of agricultural technologies and confounding influences such as weather. Here, we evaluate the effect of climate change on United States (US) maize yields in light of the productivity gains associated with the period of rapid adoption of genetically engineered (GE) seeds. We find that yield gains on the order of those experienced during the adoption of GE maize are needed to offset climate change impacts under the business-as-usual scenario, and that smaller gains, such as those associated with the pre-GE era in the 1980s and early 90s, would likely imply yield reductions below current levels. Although this study cannot identify the biophysical drivers of past and future maize yields, it helps contextualize the yield growth requirements necessary to counterbalance projected yield losses under climate change. Outside of the US, our findings have important implications for regions lagging in the adoption of new technologies which could help offset the detrimental effects of climate change.
In this paper, we evaluate a single-index polymorphic production function that relates agricultural output to temperature and precipitation. The advantage of this new approach to measuring ...agricultural vulnerability under climatic change is that a single-index measure of vulnerability can capture a range of climate responses including plateau effects. The approach identifies plateau effects in the crop yield-weather relationship and provides overall fits consistent with higher-order polynomial fitting. We apply the technique to corn, soybeans, wheat, and cotton at the USA county level. We illustrate its computation and use as a critical policy variable.
Abstract Projected increases in food demand driven by population growth coupled with heightened agricultural vulnerability to climate change jointly pose severe threats to global food security in the ...coming decades, especially for developing nations. By providing real-time and low-cost observations, satellite remote sensing has been widely employed to estimate crop yield across various scales. Most such efforts are based on statistical approaches that require large amounts of ground measurements for model training/calibration, which may be challenging to obtain on a large scale in developing countries that are most food-insecure and climate-vulnerable. In this paper, we develop a generalizable framework that is mechanism-guided and practically parsimonious for crop yield estimation. We then apply this framework to estimate crop yield for two crops (corn and wheat) in two contrasting regions, the US Corn Belt US-CB, and India’s Indo–Gangetic plain Wheat Belt IGP-WB, respectively. This framework is based on the mechanistic light reactions (MLR) model utilizing remotely sensed solar-induced chlorophyll fluorescence (SIF) as a major input. We compared the performance of MLR to two commonly used machine learning (ML) algorithms: artificial neural network and random forest. We found that MLR-SIF has comparable performance to ML algorithms in US-CB, where abundant and high-quality ground measurements of crop yield are routinely available (for model calibration). In IGP-WB, MLR-SIF significantly outperforms ML algorithms. These results demonstrate the potential advantage of MLR-SIF for yield estimation in developing countries where ground truth data is limited in quantity and quality. In addition, high-resolution and crop-specific satellite SIF is crucial for accurate yield estimation. Therefore, harnessing the mechanism-guided MLR-SIF and rapidly growing satellite SIF measurements (with high resolution and crop-specificity) hold promise to enhance food security in developing countries towards more effective responses to food crises, agricultural policies, and more efficient commodity pricing.
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