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© 2020 by the authors; licensee Asian Online Journal Publishing Group 
 

Agriculture and Food Sciences Research 
Vol. 7, No. 1, 7-15, 2020 

ISSN(E) 2411-6653/ ISSN(P) 2518-0193 
DOI: 10.20448/journal.512.2020.71.7.15 

© 2020 by the authors; licensee Asian Online Journal Publishing Group 

    
 

 
 
 
Weather and Crop Management Impact on Crop Yield Variability 

 
Rezwanul Parvez1    

Nazea Hasan Khan Chowdhury2    

 
 

( Corresponding Author)  
1Research and Planning Associate, Community College of Denver, Denver, USA. 

 
2Faculty , Department of Business & Information Technology, Front Range Community College, Boulder County 
Campus, Longmont, USA. 

 

 
Abstract 

This research is primarily designed to examine crop yield variation due to change in weather 
pattern and crop management activities by exploring a comprehensive list of factors (e.g. 
environmental, economic etc.). Given that existing literature indicates significant effects when 
farmland value is regressed on weather variables, a natural question is to ask whether major 
agricultural crop yields responds to change in historical weather pattern. To answer this question, 
this study relies on a state level panel dataset including agricultural and high-resolution weather 
data covering the period 1997-2018. Using a Seemingly Unrelated Regression (SUR) approach, 
this study estimates how crop spatial distribution patterns have impacted crop yield variation in 
response to weather in United States Greater Midwest region. Key findings indicate that changes 
in crop management activities correspond closely to estimate of both crop price and weather 
effects. Additionally, corn yield response to weather change varies with crop spatial distribution 
pattern, with distinct impacts on the magnitude and even the direction at the state level. Further, 
revenue of corn, soybeans, wheat and their respective lagged yields have positive and significant 
effect to respective crop yields. Finally, a major crop yield does vary across region due to variation 
of crop prices, precipitation and temperature. These findings have useful implications for 
agriculture sector on how historical weather trends have affected crop yield and distribution 
pattern. 

 
Keywords: Crop yield, Seemingly unrelated regression, Crop spatial distribution, Weather pattern, Crop management, Greater Midwest. 
 

Citation | Rezwanul Parvez; Nazea Hasan Khan Chowdhury 
(2020). Weather and Crop Management Impact on Crop Yield 
Variability. Agriculture and Food Sciences Research, 7(1): 7-15. 
History:  
Received: 7 November 2019 
Revised: 10 December 2019 
Accepted: 13 January 2020 
Published: 19 February 2020 
Licensed: This work is licensed under a Creative Commons 

Attribution 3.0 License  
Publisher:  Asian Online Journal Publishing Group 
 

Acknowledgement: Both authors contributed to the conception and design of 
the study. 
Funding: This study received no specific financial support. 
Competing Interests: The authors declare that they have no conflict of 
interests. 
Transparency: The authors confirm that the manuscript is an honest, 
accurate, and transparent account of the study was reported; that no vital 
features of the study have been omitted; and that any discrepancies from the 
study as planned have been explained. 
Ethical: This study follows all ethical practices during writing.   

 

 

Contents 
1. Introduction ......................................................................................................................................................................................... 8 
2. Materials and Methods ................................................................................................................................................................... 11 
3. Data Description .............................................................................................................................................................................. 11 
4. Empirical Strategy ........................................................................................................................................................................... 11 
5. Results and Discussion ................................................................................................................................................................... 12 
6. Conclusion ......................................................................................................................................................................................... 14 
References .............................................................................................................................................................................................. 15 
 

 
 

 
 
 

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Contribution of this paper to the literature 
This research is primarily designed to examine crop yield variation due to change in weather 
pattern and crop management activities by exploring a comprehensive list of factors (e.g. 
environmental, economic etc.). 

 
1. Introduction 

There has been a growing interest to examine the relation between crop productivity and weather change 
among advocates of weather change and food security, farmers, resource management professionals, and policy 
makers. A vast majority of existing literature primarily focused on investigating how crop yield respond to weather 
conditions, with emphasis on the decadal and interannual weather variability [1-3] weather extremes [4] vapor 
pressure deficits [5] atmospheric CO2 concentration [6] as well as weather variability [7]. Agricultural crop yield 
losses due to weather change are predicted to be as high as 82% by the end of 20th century for some crops [8, 9].  

Weather change is defined as a temporal and spatial variation in the distribution of temperature and 
precipitation [10]. According to National Weather Assessment, the Upper Midwest (Minnesota, Wisconsin, 
Michigan, Iowa, South Dakota, and North Dakota) and the Northern Plains of U.S. have experienced more 
variation in temperatures and precipitation as compared to southern states [11]. The long-term weather trends 
picture can effectively signal farmers to adopt suitable cropping pattern. Existing literatures have listed multiple 
adaptation measures (i.e. planting dates shifting, switch crop patterns, develop new crops, shift in crop growing 
pattern) to mitigate the negative impacts and severity of weather on crop yield and production [12-15]. 
Agricultural producers tend to follow subsequent crop rotations to maximize crop production using lands with 
optimal water and nutrient storage conditions. Thus, it is useful to investigate how changes in crop spatial 
distribution patterns over time have impacted crop yield response to weather change in U.S. Greater Midwest? 

Majority of literature have assumed that crop distribution in space remain constant over study period [16-18]. 
Empirical models (both process-based simulation models and statistical models) have been used to assess the 
potential impacts of weather on crop yields [19]. For example, a comprehensive data-driven analysis is developed 
to investigate crop pattern between counties in regulating corn yield response to weather change at the state level 
of U.S [20]. The issue of land use change and agricultural crop acreage response due to economic and biophysical 
factors is well documented focusing on Northern Great Plains agriculture [21, 22]. Thus, it would be useful to 
investigate how historical crop yields responded to economic and biophysical factors for Greater Midwest. 

Here, the functional relationships between historical observation of weather patterns and agricultural crop 
yields (for corn, soybean and wheat) are examined at the state (11) level of Greater Midwest (i.e. Illinois, Indiana, 
Kansas, Michigan, Minnesota, Missouri, Nebraska, North Dakota, Ohio, South Dakota and Wisconsin) of the 
United States (U.S.). This paper addresses the key question of how agriculture adapts to weather change through 
crop yield loss patterns. The empirical challenge associated with measuring weather change impacts on crop yields 
centers around how to organize weather information relevant to farmers’ cropping decision. This research 
developed a state-level panel of “weather data” to address this. The annual change in weather data for a state 
reflects knowledge on local weather. The yield variables are regressed on the weather data in a SUR panel 
estimation, controlling for trends and temporal effects to better examine the relation between yields and weather.   

This study develops a set of weather indices to estimate crop yield response across regions to test the 
hypothesis of spatially varying weather change impacts. This paper makes three key contributions. First, as an 
initial rigorous empirical analysis of crop yield response to weather pattern change, it extends the knowledge on 
biophysical environment change impacts on agriculture and highlights the importance of considering yield 
variation in projections of future weather change impacts. Second, the summary contributes to the understanding 
of factors driving crop yield response. Third, the paper also builds on the economic literature of environmental 
adaptation by providing evidence of adaptation when the environmental change occurs gradually (e.g., [23, 24]). 

The yield response model should generate more stable coefficients and yield predictions as compared to models 
including highly correlated weather variables. The historical pattern of crops (corn, soybean, wheat) price and yield 
for all 11 states of US Greater Midwest are presented in Figures 1- 11.  
 

 
Figure-1. Crop price and yield (Illinois) from 1997-2018. 

                            
 



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Figure-2. Crop price and yield (Indiana) from 1997-2018. 

 
 

 
Figure-3. Crop price and yield (Kansas) from 1997-2018. 

 

 
Figure-4. Crop price and yield (Michigan) from 1997-2018. 

 

 
Figure-5. Crop price and yield (Minnesota) from 1997-2018. 

                                       
 



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Figure-6. Crop price and yield (Missouri) from 1997-2018. 

 

 
Figure-7. Crop price and yield (Nebraska) from 1997-2018. 

                                    
 

 
Figure-8. Crop price and yield (North Dakota) from 1997-2018. 

 
 

 
Figure-9. Crop price and yield (Ohio) from 1997-2018. 

 
 



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Figure-10. Crop price and yield (South Dakota) from 1997-2018. 

 

 
Figure-11. Crop price and yield (Wisconsin) from 1997-2018. 

 
Understanding the connection among major crop yields, crop prices, weather, and other input prices are vital 

to stakeholders who are concerned with addressing weather change and improving global food security. Also, 
researchers and policy-makers are deeply engaged to better understand the effects of weather change on the 
agricultural landscape of the greater Midwest. The importance of estimating functional relationships between 
historical observations of weather and yields are well cited in scientific literature. The results discussed here will 
help forecast future crop yield trends in response to change in weather pattern. Further, this research will help 
policy-makers and producers make better-informed crop production decisions in a changing weather scenario. 

 

2. Materials and Methods 
This section empirically examines the effects of weather change (weather factors like monthly average 

precipitation and temperature, lagged crop prices,) on multiple crops yields (corn, soybeans, wheat) loss. The 
weather data is constructed to investigate weather trends at the state level. The functional relationship is presented 
on how crop yields (current year) correlate with current and last year crop prices, lag crop yields, crop revenue, and 
weather variables like precipitation, temperature, using longitudinal data. Here, dependent variables of the stated 
regression model for each three major crop choices (corn, wheat, and soybean) are measured at yield per bushel. 
Further, a seemingly unrelated regression (SUR) approach is employed with spatial autoregressive terms to 
capture the weather effects on crop yield loss.  

This is because statistical models including only on spatial variation (i.e. cross-sectional studies) are very prone 
to omitted variable bias, whereas those including only on temporal variation (i.e. time series studies) are often 
subject to significant errors in estimates of temperature sensitivity. These errors can happen due to limited 
temporal variation in temperature compared to other weather variables, as well as strong temporal correlation 
between temperature and rainfall or radiation. 
 

3. Data Description 
The analyses presented in this study compiles agricultural and climatologic data. State-level annual data on 

crop yields are collected from NASS surveys from 1997 to 2018. Weather data (e.g. precipitation and temperature) 
for the month of March, April, May, June, July, and August for last 22 years come from the National Climatic Data 
Center [25]. Information on prices and yields of corn, soybeans, and wheat for all eleven Midwest states (e.g. 
North Dakota, South Dakota, Nebraska, Minnesota, Iowa, Missouri, Wisconsin, Illinois, Kansas, Michigan, 
Indiana, and Ohio) for the period 1997 to 2018 year are obtained from the Census of Agriculture (United States 
Department of Agricultural, National Agricultural Statistics Service). Major crops (e.g. corn, soybeans, and wheat) 
yield and price data are lagged. Prices data are adjusted for inflation using GDP Price deflator (2017 as base year). 
The GDP price deflator data are collected from the Federal Reserve Economic Data, Economic Research Division. 
Crops (e.g. corn, soybeans, and wheat) revenue are also calculated using yield and price information.  
 

4. Empirical Strategy 
The empirical strategy relies on employing a Seemingly Unrelated Regression (SUR) model to examine the 

marginal effects of weather factor change on agricultural crop yield at the state level. A “full information maximum 
likelihood” approach is used to examine the effects. The right-hand side of estimated regression equations includes 



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no endogenous variables and assume correlation of error term among equations. Heteroskedasticity is also almost 
certain since presence of multiple dependent variables. The regression equations can be expressed in the form of 
stacked regression. 

𝑌 = 𝑋𝛽 + 𝑢                                                (1) 
Where, Y is a set of dependent variables of crop (corn, soybeans and wheat) yields. X is a set of independent 

variables (crop prices, precipitation, and temperature for the month of March, April, May, June, July, and August, 

crop revenue etc.). Further, 𝑢 is error term. 𝛽  is parameter estimates in the proposed model. 
 

5. Results and Discussion 
Summary statistics are reported in Table 1. The estimation results of Equation 1 are reported in Tables 2-4. 

The underlying identification assumption is that a state experiencing change in weather pattern would have 
changed its management activities of agricultural crops differently from a state not experiencing any change, after 
purging off state-level trends and nation-level shocks.  

 
Table-1. Summary statistics. 

Variable name Observation Mean Standard deviation Min Max 

corn_yield 242 143.98 24.45 75.00 210.00 
soybeans_yield 242 41.79 8.05 20.00 65.00 
wheat_yield 242 54.28 13.82 24.30 89.00 
price_corn 242 3.83 1.36 2.18 7.92 
price_soybeans 242 9.49 2.65 5.48 15.87 
price_wheat 242 5.12 1.54 2.47 9.07 
Lagged yield_corn 231 142.76 23.80 75.00 201.00 
Lagged yield_soybeans 231 41.37 7.79 20.00 61.00 
Lagged yield_wheat 231 54.03 13.82 24.30 89.00 
Precipitation_March 242 2.07 1.32 0.15 6.81 
Precipitation_April 242 3.12 1.57 0.50 9.61 
Precipitation_May 242 3.96 1.51 0.81 9.60 
Precipitation_june 242 4.28 1.58 1.29 9.44 
Precipitation_July 242 3.50 1.37 0.75 8.18 
Precipitation_august 242 3.32 1.21 0.62 6.94 
Temperature_march 242 36.73 8.13 16.30 58.10 
Temperature_april 242 48.32 6.08 31.30 60.90 
Temperature_may 242 59.24 5.05 47.40 72.50 
Temperature_june 242 68.65 4.32 59.10 78.10 
Temperature_july 242 73.34 4.19 63.50 84.90 
Temperature_august 242 71.31 4.16 60.90 83.00 
Revenue_corn 242 550.62 203.35 245.97 1188.02 
Revenue_soybeans 242 398.84 138.72 124.43 721.18 
Revenue_wheat 242 275.30 103.92 109.63 648.94 

 
Table-2. Results of seemingly unrelated regression (Corn equation). 

Dependent variable: Corn yield 

Explanatory variables Coefficient and standard deviation P-value 

Price_corn 
-0.29*** 

(1.33) 
0.00 

Price_soybeans 
0.78 

(0.80) 
0.33 

Price_wheat 
0.16 

(0.90) 
0.86 

Lagged yield_corn 
0.11*** 
(0.04) 

0.00 

Lagged yield_soybeans 
0.04 

(0.12) 
0.70 

Lagged yield_wheat 
0.020 
(0.07) 

0.78 

Precipitation_march 
-0.36 
(0.45) 

0.42 

Precipitation_April 
-0.58 
(0.39) 

0.14 

Precipitation_May 
-0.27 
(0.33) 

0.41 

Precipitation_June 
-0.36 
(0.34) 

0.29 

Precipitation_july 
0.72* 
(0.38 

0.06 

Precipitation_August 
1.07** 
(0.43) 

0.01 

Temperature_March 
0.29*** 
(0.11) 

0.01 

Temperature_April 
-0.02 
(0.14) 

0.92 

Temperature_May 
0.34* 
(0.18) 

0.06 

Temperature_June 
0.13 

(0.24) 
0.59 

Temperature_July 
0.20 

(0.28) 
0.46 

Temperature_august 
-0.73*** 

(0.22) 
0.00 

Revenue_corn 0.19*** 0.00 



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(0.009) 

Revenue_soybeans 
0.002 
(0.01) 

0.86 

Revenue_wheat 
0.005 
(0.01) 

0.68 

Constant 
109.85*** 

(14.40) 
0.00 

Note: The symbols ***, **, and * indicate statistical significance at α=0.01, α=0.05, and α=0.10, respectively. 
Numbers in parentheses are the standard errors of the parameter estimates. 

 
Table-3. Results of seemingly unrelated regression (Soybean Equation). 

Dependent variable: Soybeans yield 

Explanatory variables Coefficient and standard deviation P-value 

Price_corn 0.33 
(0.36) 

0.35 

Price_soybeans 3.67*** 
(0.22) 

0.00 

Price_wheat 0.12 
(0.24) 

0.61 

Lagged yield_corn -0.010 
(0.010) 

0.34 

Lagged yield_soybeans 0.12*** 
(0.03) 

0.00 

Lagged yield_wheat 0.02 
(0.02) 

0.26 

Precipitation_march -0.04 
(0.12) 

0.76 

Precipitation_April -0.13 
(0.11) 

0.21 

Precipitation_May 0.07 
(0.09) 

0.43 

Precipitation_June (0.03) 
(0.09) 

0.74 

Precipitation_july 0.13 
(0.10) 

0.20 

Precipitation_August (0.47) *** 
(0.11) 

0.00 

Temperature_March 0.04 
(0.03) 

0.18 

Temperature_april -0.04 
(0.040) 

0.30 

Temperature_May 0.04 
(0.05) 

0.41 

Temperature_June 0.09 
(0.06) 

0.16 

Temperature_July 0.06 
(0.07) 

0.45 

Temperature_august -0.17*** 
(0.06) 

0.00 

Revenue_corn -0.002 
(0.002) 

0.44 

Revenue_soybeans 0.09*** 
(0.003) 

0.00 

Revenue_wheat -0.004 
(0.003) 

0.18 

Constant 32.84*** 
(3.88) 

0.00 

Note: The symbols *** indicate statistical significance at α=0.01. 
Numbers in parentheses are the standard errors of the parameter estimates. 

 
The SUR approach used here includes longitudinal panel data for eleven Midwest states of three major crop 

yields over 22 years (1997- 2018). Key findings indicated, in all three-crop yield equation, price of soybeans 
positively related to its respective yields whereas corn and wheat prices are negatively related to its respective 
yields Tables 2-4.  Also, the relationship between crop yield and prices are statistically significant. In addition, 
lagged price of corn and wheat also negatively related to their respective yields. Last year corn yields positively 
related to current year corn yields and also statistically significant. Furthermore, revenue of corn also positively 
related to corn yields and also statistically significant. Moreover, soybeans revenue and lagged yields also 
positively related to soybeans yields and statistically significant. Further, wheat revenue and lagged yields also 
positively related to its current year yield and statistically significant. Together with regional trends in 
temperature and precipitation, these estimates can partly explain the crop expansion pattern over the past few 
decades. For instance, higher yield of corn, soybeans, and wheat have coincided with increase in both temperature 
and precipitation in the Dakotas and the Upper Midwest. This association has a particularly vital implication given 
the relatively large marginal effects estimated for this region. These results are also in line with the historical 
movements of corn location discussed [26, 27]. 
 
 

 



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Table-4. Results of seemingly unrelated regression (Wheat Equation). 

Dependent variable: Wheat yield 

Explanatory variables Coefficient and standard deviation P-value 

Price_corn -0.26 
(0.68) 

0.70 

Price_soybeans 0.96 
(0.41) 

0.02 

Price_wheat -8.58*** 
0.46 

0.00 

Lagged yield_corn -0.04** 
(0.020) 

0.02 

Lagged yield_soybeans 0.17*** 
(0.06) 

0.00 

Lagged yield_wheat 0.18*** 
(0.03) 

0.00 

Precipitation_march -0.28 
(0.23) 

0.22 

Precipitation_April 0.009 
(0.20) 

0.96 

Precipitation_May 0.25 
(0.17) 

0.14 

Precipitation_June -0.16 
(0.17) 

0.36 

Precipitation_july 0.32* 
(0.19) 

0.10 

Precipitation_August 0.78*** 
(0.22) 

0.00 

Temperature_March 0.03 
(0.06) 

0.61 

Temperature_april -0.04 
(0.07) 

0.63 

Temperature_May -0.07 
(0.09) 

0.44 

Temperature_June 0.06 
(0.12) 

0.63 

Temperature_July -0.003 
(0.14) 

0.98 

Temperature_august 0.008 
(0.11) 

0.94 

Revenue_corn 0.004 
(0.005) 

0.37 

Revenue_soybeans -0.01** 
(0.006) 

0.03 

Revenue_wheat 0.15*** 
(0.006) 

0.00 

Constant 39.21*** 
(7.34) 

0.00 

Note: The symbols ***, **, and * indicate statistical significance at α=0.01, α=0.05, and α=0.10, respectively. Numbers in 
parentheses are the standard errors of the parameter estimates. 

 
Average precipitation (March, April, May, June) are found to be negatively related to corn yields because pre-

planting time and during planting time precipitation adversely effects crop yield. In soybean equation, precipitation 
(for the month of March and April) are negatively related to soybeans yields as we expected. On the other hand, 
precipitation July and August are positively related to soybeans yields. In wheat equation, precipitation (for the 
month of March and June) negatively related to wheat yields. Precipitation (for the month of July and August) are 
positively related to wheat yields. Temperature also significantly affects crop yield pattern. In corn equation, 
temperature (for the month of March and May) are positively related and statistically significant to corn yields for 
current year. the average temperature (for the month of August) are negatively related and statistically significant 
to corn yield. In soybean equation, average temperature (for the month of April and August) are negatively related 
to soybeans yield in current year and statistically significant Table 3. This is because producers expect positive 
temperature for optimal growth of soybeans crop and yields before and during planting season. In wheat equation, 
average temperature (for the month of March and August) are positively related to wheat yield Table 4. 

 

6. Conclusion 
The issue of weather change impacts on crop yields variability is well discussed by existing literature. All else 

equal, the heterogeneity of multiple crop yield responses also implies a change in relative profitability per acre. As 
weather change persists, this will slowly alter cropping patterns. This study analyzes the importance of taking crop 
yield effects into consideration when evaluating weather pattern change impacts on agriculture. The scenario of 
adjustment and adaptation in crop acreage by producers is important and need to be included in policy discussion 
to avoid imprecise and even unrealistic projections of future crop losses due to weather change. The findings 
reported here are useful to see how yield responds to different economic and biophysical factors (e.g. crop prices, 
lagged yields and prices, crop revenue, temperature, precipitation). Results also indicate that precipitation before 
planting season has impact on crop yield. The impact of average temperature on crop yields is evident. Further, 
crop revenue, lagged yields of corn, wheat, and soybeans are positively related to current year yields. Moreover, 
prices of corn and wheat negatively related to current year yield. Factors like precipitation (for the month of 



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March) have negative effect on yields of corn, wheat, and soybeans. Also, temperature (for the month of March) 
positively related to yields of corn, wheat, and soybeans. The findings discussed here will help policy makers, 
researchers, producers, and landowners making more informed cropping decision as a response to change in 
weather as well as economic factors affecting crop yields.  

This research work has few limitations too. First, this analysis only highlights reduced-form relationship 
between weather pattern and crop yields and does not include other technological and socioeconomic drivers of 
crop patterns. Second, the nature of data determines the crop yields shift are not directly observed at a field level. 
Third, this study only focuses on a selected number of field crops (corn, soybean, and wheat) excluding all other 
agricultural crops. Future research is needed to incorporate plant characteristics, cultivation practices, farm 
management strategies, producers’ decision making behavior and other factors for more robust analyses. 

  

References 
[1] E. J. Colville, A. E. Carlson, B. L. Beard, R. G. Hatfield, J. S. Stoner, A. V. Reyes, and D. J. Ullman, "Sr-Nd-Pb isotope evidence for 

ice-sheet presence on Southern Greenland during the last interglacial," Science, vol. 333, pp. 620-623, 2011. 
[2] W. Schlenker and M. J. Roberts, "Nonlinear temperature effects indicate severe damages to US crop yields under climate change," 

Proceedings of National Academy of Sciences, USA, vol. 106, pp. 15594–15598, 2009.Available at: 
https://doi.org/10.1073/pnas.0906865106. 

[3] L. You, M. W. Rosegrant, S. Wood, and D. Sun, "Impact of growing season temperature on wheat productivity in China," 
Agricultural and Forest Meteorology, vol. 149, pp. 1009-1014, 2009.Available at: https://doi.org/10.1016/j.agrformet.2008.12.004. 

[4] C. Lesk, P. Rowhani, and N. Ramankutty, "Influence of extreme weather disasters on global crop production," Nature, vol. 529, pp. 
84-87, 2016.Available at: https://doi.org/10.1038/nature16467. 

[5] J. D. Ray, R. W. Gesch, T. R. Sinclair, and L. H. Allen, "The effect of vapor pressure deficit on maize transpiration response to a 
drying soil," Plant and Soil, vol. 239, pp. 113-121, 2002. 

[6] G. Sakurai, T. Iizumi, M. Nishimori, and M. Yokozawa, "How much has the increase in atmospheric CO2 directly affected past 
soybean production?," Scientific Reports, vol. 4, pp. 1-4, 2014. 

[7] G. Leng, X. Zhang, M. Huang, G. R. Asrar, and L. R. Leung, "The role of climate covariability on crop yields in the conterminous 
United States," Scientific Reports, vol. 6, pp. 1-11, 2016.Available at: https://doi.org/10.1038/srep33160. 

[8] M. Gammans, P. Mérel, and A. Ortiz-Bobea, "Negative impacts of climate change on cereal yields: Statistical evidence from 
France," Environmental Research Letters, vol. 12, pp. 1-9, 2017. 

[9] B. Schauberger, S. Archontoulis, A. Arneth, J. Balkovic, P. Ciais, D. Deryng, J. Elliott, C. Folberth, N. Khabarov, and C. Müller, 
"Consistent negative response of US crops to high temperatures in observations and crop models," Nature Communications, vol. 8, 
pp. 1-9, 2017.Available at: https://doi.org/10.1038/ncomms13931. 

[10] S. Hsiang, "Climate econometrics," Annual Review of Resource Economics, vol. 8, pp. 43-75, 2016. 
[11] J. M. Melillo, T. Richmond, and G. Yohe, "Climate change impacts in the United States," Third National Climate Assessment2014. 
[12] T. M. Osborne and T. R. Wheeler, "Evidence for a climate signal in trends of global crop yield variability over the past 50 years," 

Environmental Research Letters, vol. 8, pp. 1-9, 2013.Available at: https://doi.org/10.1088/1748-9326/8/2/024001. 
[13] A. S. Cohn, L. K. VanWey, S. A. Spera, and J. F. Mustard, "Cropping frequency and area response to climate variability can exceed 

yield response," Nature Climate Change, vol. 6, pp. 601-604, 2016.Available at: https://doi.org/10.1038/nclimate2934. 
[14] A. J. Challinor, J. Watson, D. B. Lobell, S. Howden, D. Smith, and N. Chhetri, "A meta-analysis of crop yield under climate change 

and adaptation," Nature Climate Change, vol. 4, pp. 287-291, 2014.Available at: https://doi.org/10.1038/nclimate2153. 
[15] K. Waha, C. Müller, A. Bondeau, J. P. Dietrich, P. Kurukulasuriya, J. Heinke, and H. Lotze-Campen, "Adaptation to climate change 

through the choice of cropping system and sowing date in Sub-Saharan Africa," Global Environmental Change, vol. 23, pp. 130–143, 
2013.Available at: https://doi.org/10.1016/j.gloenvcha.2012.11.001. 

[16] S. Asseng, F. Ewert, and P. Martre, "Rising temperatures reduce global wheat production," Nature Clim Change, vol. 5, pp. 143–
147, 2015. 

[17] T. Lizumi, J.-J. Luo, A. J. Challinor, G. Sakurai, M. Yokozawa, H. Sakuma, M. E. Brown, and T. Yamagata, "Impacts of El Niño 
Southern oscillation on the global yields of major crops," Nature Communications, vol. 5, pp. 1-7, 2014.Available at: 
https://doi.org/10.1038/ncomms4712. 

[18] B. Liu, S. Asseng, C. Müller, F. Ewert, J. Elliott, D. B. Lobell, P. Martre, A. C. Ruane, D. Wallach, and J. W. Jones, "Similar 
estimates of temperature impacts on global wheat yield by three independent methods," Nature Climate Change, vol. 6, pp. 1130-
1136, 2016.Available at: https://doi.org/10.1038/nclimate3115. 

[19] D. B. Lobell and M. B. Burke, "On the use of statistical models to predict crop yield responses to climate change," Agricultural and 
Forest Meteorology, vol. 150, pp. 1443-1452, 2010.Available at: https://doi.org/10.1016/j.agrformet.2010.07.008. 

[20] G. Leng and M. Huang, "Crop yield response to climate change varies with crop spatial distribution pattern," Scientific Reports, vol. 
7, pp. 1-10, 2017.Available at: https://doi.org/10.1038/s41598-017-01599-2. 

[21] R. Parvez, B. D. Madurapperuma, and D. Ripplinger, "Modeling land use pattern change analysis in the Northern Great Plains: A 
novel approach," presented at the Presented as a paper at Agricultural and Applied Economics Association (AAEA) & Western 
Agricultural Economics Association (WAEA) Joint Annual Meeting, San Francisco, CA, 2015. 

[22] R. Parvez, D. C. Roberts, and D. Ripplinger, "Factors impacting crop acreage decision: A case study of North Dakota agriculture," 
Agricultural Development, vol. 3, pp. 16-36, 2018.Available at: https://doi.org/10.20448/journal.523.2018.31.16.36. 

[23] R. Hornbeck, "The enduring impact of the American Dust Bowl: Short-and long-run adjustments to environmental catastrophe," 
American Economic Review, vol. 102, pp. 1477-1507, 2012.Available at: https://doi.org/10.1257/aer.102.4.1477. 

[24] R. Hornbeck and K. Pinar, "The historically evolving impact of the ogallala aquifer: Agricultural adaptation to groundwater and 
drought," American Economic Journal: Applied Economics, vol. 6, pp. 190–219, 2014.Available at: 
https://doi.org/10.1257/app.6.1.190. 

[25] National Climatic Data Center, "National Centers for Environmental Information,  Asheville NC 28801-5001, USA." Retrieved 
from: https://www.ncdc.noaa.gov/, https://www1.ncdc.noaa.gov/pub/data/cirs/climdiv/?C=M;O=D, 2018. 

[26] A. L. Olmstead and P. W. Rhode, "Adapting North American wheat production to climatic challenges, 1839–2009," Proceedings of 
the National Academy of Sciences, vol. 108, pp. 480-485, 2011.Available at: https://doi.org/10.1073/pnas.1008279108. 

[27] J. M. Beddow and P. G. Pardey, "Moving matters: The effect of location on crop production," The Journal of Economic History, vol. 
75, pp. 219-249, 2015.Available at: https://doi.org/10.1017/s002205071500008x. 

  
 
 

 
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