Climatic influence on corn sowing date in the Midwestern United States

Choi, Yun-Sang; Gim, Hyeon Ju; Ho, Chin Siong; Jeong, Sooyeon; Park, Sojung; Hayes, Michael J.

doi:10.1002/joc.4799

Cited by 22 publications

(16 citation statements)

References 41 publications

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“…Because the lack of high‐resolution crop calendars is a significant source of uncertainty in modeling (Elliott et al, ), monitoring (Laborte et al, ; Whitcraft et al, ), and forecasting crop production (Iizumi et al, , ), efforts for better understanding and modeling of sowing dates have been conducted (Choi et al, ; Mathison et al, ; Waha et al, ). Plausible estimates of sowing dates are key to reducing the uncertainty because dates of harvesting and other phenological events (e.g., flowering) can be accurately estimated if observed sowing dates are fed into calibrated crop phenology models (Iizumi et al, ; van Bussel et al, ).…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, even in regions where climate is a determinant of sowing dates, the main climatic constraints vary by region and crop, as exemplified by the relatively greater importance of temperature than precipitation for maize in the American Midwest (Choi et al, ), the timing of rainy (dry) season onset for rice‐wheat rotation in India (Mathison et al, ), the seasonality of monsoons, flooding and salinity intrusion for triple rice cropping in the Vietnamese Mekong Delta (Kotera et al, ), and soil moisture levels for millet and sorghum in the dry tropics (Marteau et al, ).…”

Section: Discussionmentioning

confidence: 99%

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Modeling the Global Sowing and Harvesting Windows of Major Crops Around the Year 2000

Iizumi

Kim

Nishimori

2019

J Adv Model Earth Syst

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The lack of spatially detailed crop calendars is a significant source of uncertainty in modeling, monitoring, and forecasting crop production. In this paper, we present a rule-based model to estimate the sowing and harvesting windows of major crops over the global land area. The model considers field workability due to snow cover and heavy rainfall in addition to crop biological requirements for heat, chilling, and moisture. Using daily weather data for the period 1996-2005 as model input, we derive calendars for maize, rice, winter and spring wheat, and soybeans around the year 2000 with a spatial resolution of 0.5°in latitude and longitude. Separate calendars for rainfed and irrigated conditions and three representative varieties (short-, medium-and long-season varieties) are estimated. The daily probabilities of sowing and harvesting derived using the model well capture the major characteristics of reported calendars. Our modeling reveals that field workability is an important determinant of sowing and harvesting dates and that multicropping patterns influence the calendars of individual crops. The case studies show that the model is capable of capturing multicropping patterns such as triple rice cropping in Bangladesh, double rice cropping in the Philippines, winter wheat-maize rotations in France, and maize-winter wheat-soybean rotations in Brazil. The model outputs are particularly valuable for agricultural and hydrological applications in regions where existing crop calendars are sparse or unreliable. Plain Language SummaryThis manuscript describes a numerical model to estimate location-specific sowing and harvesting dates of crops over the globe. Ten-year-long daily weather data and a few coefficients that represent the physiological characteristics of a crop (for instance, the amount of water needs to complete the life cycle of an annual crop) are only inputs to the model. Comparisons with the reported crop calendars indicate that the model well reproduces calendars of maize, rice, winter and spring wheat, and soybean around the year 2000 in major crop-producing countries. We also find that snow cover and heavy rainfall, which influence field workability but have not considered in earlier modeling, are important to estimate sowing and harvesting dates and multicropping patterns (for instance, a combination of winter and summer crops) affect the calendar of individual crops. Our findings are useful when simulating the responses of crop calendars to climate change.In the SAGE data set, average planting and harvesting windows for 19 crops, including major crops (maize, rice, wheat, and soybean), around the 1990s and early 2000s are reported. Different growing seasons of a crop, such as spring and winter wheat and main-and second-season maize and rice, are available in SAGE, but this distinction is not available in MIRCA2000. Instead, average planting and harvesting months for 23 crops, including the major crops, in 1998-2002 are available for each of the irrigated and rainfed conditions in MIRCA2000. Howeve...

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Modeling the Global Sowing and Harvesting Windows of Major Crops Around the Year 2000

Iizumi

Kim

Nishimori

2019

J Adv Model Earth Syst

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“…For example, Kucharik [ 65 ] found that multidecadal trends of earlier planting contributed to rising yields (0.06 and 0.14 Mg ha -1 for each additional day of earlier planting) during 1979 to 2005 in 6 out of 12 central US states. Early planting offers several benefits such as an extended growing season allowing corn plants to accumulate more photosynthates, attain physiological maturity before the killing fall frost, and flowering before midsummer heat stress [ 65 – 66 ]. Our study indicates that either of the earlier planting date scenarios could help avoid the high temperature episodes during the R1 stage compared to BAUPD.…”

Section: Discussionmentioning

confidence: 99%

Projected climate and agronomic implications for corn production in the Northeastern United States

et al. 2018

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Corn has been a pillar of American agriculture for decades and continues to receive much attention from the scientific community for its potential to meet the food, feed and fuel needs of a growing human population in a changing climate. By midcentury, global temperature increase is expected to exceed 2°C where local effects on heat, cold and precipitation extremes will vary. The Northeast United States is a major dairy producer, corn consumer, and is cited as the fastest warming region in the contiguous U.S. It is important to understand how key agronomic climate variables affect corn growth and development so that adaptation strategies can be tailored to local climate changes. We analyzed potential local effects of climate change on corn growth and development at three major dairy locations in the Northeast (Syracuse, New York; State College, Pennsylvania and Landisville, Pennsylvania) using downscaled projected climate data (2000–2100) from nine Global Climate Models under two emission pathways (Representative Concentration Pathways (RCP) 4.5 and 8.5). Our analysis indicates that corn near the end of the 21st century will experience fewer spring and fall freezes, faster rate of growing degree day accumulation with a reduction in time required to reach maturity, greater frequencies of daily high temperature ≥35°C during key growth stages such as silking-anthesis and greater water deficit during reproductive (R1-R6) stages. These agronomic anomalies differ between the three locations, illustrating varying impacts of climate change in the more northern regions vs. the southern regions of the Northeast. Management strategies such as shifting the planting dates based on last spring freeze and irrigation during the greatest water deficit stages (R1-R6) will partially offset the projected increase in heat and drought stress. Future research should focus on understanding the effects of global warming at local levels and determining adaptation strategies that meet local needs.

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“…Wetter springs will likely make timely spring planting operations difficult and drier summers will increase drought risk [ 22 , 27 ]. Few recent studies explicitly model changes in field workability resulting from climate change [ 28 – 31 ], despite the clear connection between climate and field workability [ 32 ].…”

Section: Introductionmentioning

confidence: 99%

Changes in field workability and drought risk from projected climate change drive spatially variable risks in Illinois cropping systems

2017

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As weather patterns become more volatile and extreme, risks introduced by weather variability will become more critical to agricultural production. The availability of days suitable for field work is driven by soil temperature and moisture, both of which may be altered by climate change. We projected changes in Illinois season length, spring field workability, and summer drought risk under three different emissions scenarios (B1, A1B, and A2) down to the crop district scale. Across all scenarios, thermal time units increased in parallel with a longer frost-free season. An increase in late March and Early April field workability was consistent across scenarios, but a decline in overall April through May workable days was observed for many cases. In addition, summer drought metrics were projected to increase for most scenarios. These results highlight how the spatial and temporal variability in climate change may present unique challenges to mitigation and adaptation efforts.

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Climatic influence on corn sowing date in the Midwestern United States

Cited by 22 publications

References 41 publications

Modeling the Global Sowing and Harvesting Windows of Major Crops Around the Year 2000

Modeling the Global Sowing and Harvesting Windows of Major Crops Around the Year 2000

Projected climate and agronomic implications for corn production in the Northeastern United States

Changes in field workability and drought risk from projected climate change drive spatially variable risks in Illinois cropping systems

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