scholarly journals Machine Learning-Based Modeling of Spatio-Temporally Varying Responses of Rainfed Corn Yield to Climate, Soil, and Management in the U.S. Corn Belt

2021 ◽  
Vol 4 ◽  
Author(s):  
Tianfang Xu ◽  
Kaiyu Guan ◽  
Bin Peng ◽  
Shiqi Wei ◽  
Lei Zhao
Keyword(s):  
Machine Learning ◽  
Organic Matter ◽  
Random Forest ◽  
Planting Date ◽  
Yield Variability ◽  
Corn Belt ◽  
Corn Yield ◽  
Water Capacity ◽  
Available Water Capacity ◽  
The U.S

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.

scholarly journals Trends and Variability in U.S. Corn Yields Over the Twentieth Century

2005 ◽  
Vol 9 (1) ◽  
pp. 1-29 ◽  
Author(s):  
Christopher J. Kucharik ◽  
Navin Ramankutty
Keyword(s):  
Twentieth Century ◽  
Growth Rates ◽  
The United States ◽  
Yield Variability ◽  
Corn Belt ◽  
Corn Yield ◽  
County Level ◽  
Agricultural Revolution ◽  
Decadal Scale ◽  
The U.S

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.

scholarly journals Determination of the effects of tillage on the productivity of a sandy loam soil using soil productivity models

2021 ◽  
Vol 67 (No. 3) ◽  
pp. 108-115
Author(s):  
Tanko Bako ◽  
Ezekiel Ambo Mamai ◽  
Istifanus Akila Bardey
Keyword(s):  
Organic Matter ◽  
Iron Oxide ◽  
Bulk Density ◽  
Aluminium Oxide ◽  
Available Phosphorus ◽  
Soil Productivity ◽  
Water Capacity ◽  
Available Water ◽  
Available Water Capacity ◽  
Tillage Methods

Based on the hypothesis that soil properties and productivity components should be affected by different tillage methods, field and laboratory experiments were conducted to study the effects of zero tillage (ZT), one pass of disc plough tillage (P), one pass of disc plough plus one pass of disc harrow tillage (PH) and one pass of disc plough plus two passes of disc harrow tillage (PHH) on the distribution of the bulk density, available water capacity, pH, organic matter, available phosphorus, iron oxide and aluminium oxide at different soil depths, and their effects on the soil productivity. The available water capacity, pH, organic matter and available phosphorus were found to increase with the degree of tillage, while the bulk density, iron oxide and aluminium oxide were found to decrease with the degree of tillage. The results show that the soil productivity index was significantly (P ≤ 0.05) affected by the tillage methods and found to increase with the degree of tillage.

scholarly journals Climate Variability and the U.S. Corn Belt: ENSO and AO Episode-Dependent Hydroclimatic Feedbacks to Corn Production at Regional and Local Scales*

2015 ◽  
Vol 19 (6) ◽  
pp. 1-32 ◽  
Author(s):  
Olivia Kellner ◽  
Dev Niyogi
Keyword(s):  
El Niño ◽  
El Nino ◽  
Growing Season ◽  
Yield Potential ◽  
Corn Belt ◽  
Corn Yield ◽  
Corn Production ◽  
Enso Events ◽  
Temperature And Precipitation ◽  
The U.S

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.

Understanding the impact of sub-seasonal meteorological variability on corn yield in the U.S. Corn Belt

2020 ◽  
Vol 724 ◽  
pp. 138235
Author(s):  
Hao Jiang ◽  
Hao Hu ◽  
Shaowen Wang ◽  
Yibin Ying ◽  
Tao Lin
Keyword(s):  
Corn Belt ◽  
Corn Yield ◽  
The Impact ◽  
The U.S

scholarly journals Coupling machine learning and crop modeling improves crop yield prediction in the US Corn Belt

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Mohsen Shahhosseini ◽  
Guiping Hu ◽  
Isaiah Huber ◽  
Sotirios V. Archontoulis
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Research Question ◽  
Hybrid Approach ◽  
Corn Belt ◽  
Importance Measure ◽  
Corn Yield ◽  
Yield Prediction ◽  
Crop Modeling ◽  
The Us

AbstractThis study investigates whether coupling crop modeling and machine learning (ML) improves corn yield predictions in the US Corn Belt. The main objectives are to explore whether a hybrid approach (crop modeling + ML) would result in better predictions, investigate which combinations of hybrid models provide the most accurate predictions, and determine the features from the crop modeling that are most effective to be integrated with ML for corn yield prediction. Five ML models (linear regression, LASSO, LightGBM, random forest, and XGBoost) and six ensemble models have been designed to address the research question. The results suggest that adding simulation crop model variables (APSIM) as input features to ML models can decrease yield prediction root mean squared error (RMSE) from 7 to 20%. Furthermore, we investigated partial inclusion of APSIM features in the ML prediction models and we found soil moisture related APSIM variables are most influential on the ML predictions followed by crop-related and phenology-related variables. Finally, based on feature importance measure, it has been observed that simulated APSIM average drought stress and average water table depth during the growing season are the most important APSIM inputs to ML. This result indicates that weather information alone is not sufficient and ML models need more hydrological inputs to make improved yield predictions.

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