Corn Yield and Climate in the Corn Belt

1936 ◽  
Vol 26 (1) ◽  
pp. 88 ◽  
Author(s):  
John Kerr Rose
Keyword(s):  
1988 ◽  
Vol 2 (4) ◽  
pp. 410-413 ◽  
Author(s):  
Elifas N. Alcantara ◽  
Donald L. Wyse

Glyphosate was evaluated as a preharvest treatment for enhancing corn kernel dry down and quackgrass control. Glyphosate at 0.4, 0.8, 1.3, and 1.7 kg ae/ha was applied to corn before physiological maturity (kernel moisture 44 to 47%) and at physiological maturity (kernel moisture 35 to 39%). Three weeks after treatment, grain moisture of plants treated at physiological maturity was 2.3 to 6.9% less than that of plants treated before physiological maturity which was 2.2 to 5.5% less than that of untreated plants. Glyphosate did not increase corn kernel drying effectively under high humidity conditions. Glyphosate applied to pre-physiologically-mature corn controlled quackgrass 49 to 64% in the fall of 1984 and 69 to 91% in the fall of 1985. None of the glyphosate treatments reduced corn yield. Glyphosate applied preharvest above the corn canopy may increase the rate of corn kernel drying and may control fall quackgrass in the northern corn belt.


2008 ◽  
Vol 7 (1) ◽  
pp. 1-25 ◽  
Author(s):  
E. C. A. Runge ◽  
John F. Benci
Keyword(s):  

2021 ◽  
Vol 12 ◽  
Author(s):  
Mohsen Shahhosseini ◽  
Guiping Hu ◽  
Saeed Khaki ◽  
Sotirios V. Archontoulis

We investigate the predictive performance of two novel CNN-DNN machine learning ensemble models in predicting county-level corn yields across the US Corn Belt (12 states). The developed data set is a combination of management, environment, and historical corn yields from 1980 to 2019. Two scenarios for ensemble creation are considered: homogenous and heterogenous ensembles. In homogenous ensembles, the base CNN-DNN models are all the same, but they are generated with a bagging procedure to ensure they exhibit a certain level of diversity. Heterogenous ensembles are created from different base CNN-DNN models which share the same architecture but have different hyperparameters. Three types of ensemble creation methods were used to create several ensembles for either of the scenarios: Basic Ensemble Method (BEM), Generalized Ensemble Method (GEM), and stacked generalized ensembles. Results indicated that both designed ensemble types (heterogenous and homogenous) outperform the ensembles created from five individual ML models (linear regression, LASSO, random forest, XGBoost, and LightGBM). Furthermore, by introducing improvements over the heterogenous ensembles, the homogenous ensembles provide the most accurate yield predictions across US Corn Belt states. This model could make 2019 yield predictions with a root mean square error of 866 kg/ha, equivalent to 8.5% relative root mean square and could successfully explain about 77% of the spatio-temporal variation in the corn grain yields. The significant predictive power of this model can be leveraged for designing a reliable tool for corn yield prediction which will in turn assist agronomic decision makers.


2005 ◽  
Vol 9 (1) ◽  
pp. 1-29 ◽  
Author(s):  
Christopher J. Kucharik ◽  
Navin Ramankutty

Abstract The United States is currently responsible for 40%–45% of the world’s corn supply and 70% of total global exports [the U.S. Department of Agriculture–National Agricultural Statistics Service (USDA–NASS)]. Therefore, analyses of the spatial and temporal patterns of historical U.S. corn yields might provide insight into future crop-production potential and food security. In this study, county-level maize yield data from 1910 to 2001 were used to characterize the spatial heterogeneity of yield growth rates and interannual yield variability across the U.S. Corn Belt. Widespread decadal-scale changes in corn yield variability and yield growth rates have occurred since the 1930s across the Corn Belt, but the response has varied substantially with geographic location. Northern portions of the Great Plains have experienced consistently high interannual corn yield variability, averaging 30%–40% relative to the mean. Increasing usage of irrigation in Nebraska, Kansas, and Texas, since the 1950s, has helped boost yields by 75%–90% over rain-fed corn, creating a yield gap of 2–4 T ha−1 between irrigated and nonirrigated corn that could potentially be exploited in other regions. Furthermore, irrigation has reduced interannual variability by a factor of 3 in these same regions. A small region from eastern Iowa into northern Illinois and southern Wisconsin has experienced minimal interannual yield variability, averaging only 6%–10% relative to mean yields. This paper shows that the choice of time period used for statistical analysis impacted conclusions drawn about twentieth-century trends in corn yield variability. Widespread increases in yield variability were apparent from 1950 onward, but were not significant over the entire 1930–2001 period. There is also evidence that yield variability decreased from the early 1990s to 2001. Corn yield growth rates peaked at an annual-average rate of 3%–5% in the 1960s (124.5 kg ha−1 yr−1), but have steadily declined to a relative rate of 0.78% yr−1 (49.2 kg ha−1 yr−1) during the 1990s. A general inverse relationship between increasing corn yield and decreasing yield growth rates was noted after county-level yields reached 4 T ha−1, suggesting that widespread, significant increases in corn yield are not likely to take place in the future, particularly on irrigated land, without a second agricultural revolution.


Crop Science ◽  
2020 ◽  
Vol 60 (2) ◽  
pp. 739-750 ◽  
Author(s):  
Rai Schwalbert ◽  
Telmo Amado ◽  
Luciana Nieto ◽  
Geomar Corassa ◽  
Charles Rice ◽  
...  

2021 ◽  
Vol 4 ◽  
Author(s):  
Tianfang Xu ◽  
Kaiyu Guan ◽  
Bin Peng ◽  
Shiqi Wei ◽  
Lei Zhao

Better understanding the variabilities in crop yield and production is critical to assessing the vulnerability and resilience of food production systems. Both environmental (climatic and edaphic) conditions and management factors affect the variabilities of crop yield. In this study, we conducted a comprehensive data-driven analysis in the U.S. Corn Belt to understand and model how rainfed corn yield is affected by climate variability and extremes, soil properties (soil available water capacity, soil organic matter), and management practices (planting date and fertilizer applications). Exploratory data analyses revealed that corn yield responds non-linearly to temperature, while the negative vapor pressure deficit (VPD) effect on corn yield is monotonic and more prominent. Higher mean yield and inter-annual yield variability are found associated with high soil available water capacity, while lower inter-annual yield variability is associated with high soil organic matter (SOM). We also identified region-dependent relationships between planting date and yield and a strong correlation between planting date and the April weather condition (temperature and rainfall). Next, we built machine learning models using the random forest and LASSO algorithms, respectively, to predict corn yield with all climatic, soil properties, and management factors. The random forest model achieved a high prediction accuracy for annual yield at county level as early as in July (R2 = 0.781) and outperformed LASSO. The gained insights from this study lead to improved understanding of how corn yield responds to climate variability and projected change in the U.S. Corn Belt and globally.


Author(s):  
Joe Wan ◽  
Michael Qu ◽  
Xianjun Hao ◽  
Ray Motha ◽  
John J. Qu

2015 ◽  
Vol 19 (6) ◽  
pp. 1-32 ◽  
Author(s):  
Olivia Kellner ◽  
Dev Niyogi

Abstract El Niño–Southern Oscillation (ENSO) and Arctic Oscillation (AO) climatology (1980–2010) is developed and analyzed across the U.S. Corn Belt using state climate division weather and historic corn yield data using analysis of variance (ANOVA) and correlation analysis. Findings provide insight to agroclimatic conditions under different ENSO and AO episodes and are analyzed with a perspective for potential impacts to agricultural production and planning, with findings being developed into a web-based tool for the U.S. Corn Belt. This study is unique in that it utilizes the oceanic Niño index and explores two teleconnection patterns that influence weather across different spatiotemporal scales. It is found that the AO has a more frequent weak to moderate correlation to historic yields than ENSO when correlated by average subgrowing season index values. Yield anomaly and ENSO and AO episode analysis affirms the overall positive impact of El Niño events on yields compared to La Niña events, with neutral ENSO events in between as found in previous studies. Yields when binned by the AO episode present more uncertainty. While significant temperature and precipitation impacts from ENSO and AO are felt outside of the primary growing season, correlation between threshold variables of episode-specific temperature and precipitation and historic yields suggests that relationships between ENSO and AO and yield are present during specific months of the growing season, particularly August. Overall, spatial climatic variability resulting from ENSO and AO episodes contributes to yield potential at regional to subregional scales, making generalization of impacts difficult and highlighting a continued need for finescale resolution analysis of ENSO and AO signal impacts on corn production.


2020 ◽  
Vol 724 ◽  
pp. 138235
Author(s):  
Hao Jiang ◽  
Hao Hu ◽  
Shaowen Wang ◽  
Yibin Ying ◽  
Tao Lin
Keyword(s):  

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