6. Simulation models to quantify climate effects

Simulation models have been extensively used to estimate the impact of a changing climate on productivity. In 2014, Challinor et al. [27] summarized 1700 published reports using simulation models and the number of papers has increased rapidly since that time. Simulation models provide the capability of assessing the potential impacts of the change in temperature and precipitation under a given CO2 regime and often models using the different emission scenarios to determine the expected temperature and precipitation parameters which are then placed into crop simulation models [54, 55]. It has been found that an ensemble of crop models provides a more rigorous approach to estimating crop responses to climate. This is being conducted under the Agricultural Model Implementation and Improvement Project (AgMIP) framework as described by Rosenzweig et al. [56]. Bassu et al. [57] used this framework to compare 23 different corn models and found temperature decreased yield by approximately 0.5 Mg ha<sup>1</sup><sup>C</sup><sup>1</sup> while doubling the CO2 from 360 to 720 <sup>μ</sup>mol mol<sup>1</sup> increased yield by 7.5% across all models and sites. They concluded that temperature increases would be the dominant factor affecting corn yields. Zhao et al. [58] summarized a number of published results and found for each 1C increase, corn yields decreased by 7.4%. Jin et al. [59] used the Agricultural Production Systems sIMulator (APSIM) model to evaluate the effect of different CO2 scenarios (RCP4.5 and RCP8.5) for corn production in the US and found drought will be the largest factor affecting production. However, they stated that combined impacts of temperature and water stress need to be evaluated in breeding programs and adaptation strategies [59]. Earlier, Jin et al. [60] evaluated the algorithms in 16 different corn models and concluded that heat and drought stress was best simulated when models used event-based heat and water stress descriptions, accounted for nighttime temperature stresses, and evaluated the interactions of multiple stresses. Crop models allow for an assessment of the role of genetics and management on productivity for a range of present and future environmental conditions. Hatfield and Walthall [31] utilized this concept as the G x E x M (genetics environment management) framework to determine how these interactions would need to be understood to provide food security for the future population growth.

efficiency of plant growth relative to the changes in temperature and the accumulation of

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The Global Agro-ecological zones model (GAEZ) categorizes areas suitable for crop production by climate, soil, terrain, management, and the specific growth limitations of crops, among others [65, 66]. One essential concept of GAEZ climate module is the temperature growing period (LGPt), where air temperature is used as a proxy to estimate days of the growing period with optimal, sub-optimal, and no suitable crop production conditions for a specific crop. The growing period L is defined as the number of days with average daily temperature > 5C (i.e., LGPt5). The corn-specific LGPt's are summarized in Table 1. For example, assume a temperate corn cultivar for grain production with a total growing period between 90 and 180 days. During this period average daily air temperature shall not decrease below 5C, and the number of days with daily average air temperature between 10 and 15C shall be below ⅟<sup>5</sup> of the total growing period to reach optimum growing conditions. In addition to air temperature, the length of the growing period is further limited by the moisture regime, defined as actual

The GAEZ model also estimates potential yield of a specific crop in a specific agro-ecological zone, and applies constraint factors, such as heat or water stress, to calculate actual yield and yield gap. For example, periods of potential water stress occur when actual ET is below the total water requirement of a crop, maximum ET, and the difference between both cannot be compensated by precipitation, plant available water, or irrigation. Maximum ET is calculated as reference ET multiplied by crop coefficient kc. Maximum ET is crop specific and changes during crop development by applying crop-development specific kc values (Figure 6). The derived water stress data is then used to calculate yield constraining factors. The GAEZ model

Cultivars Tropics lowland Tropics highland Subtropics-temperate Subtropics-temperate

90–120 120–300 90–180 105–180

LGPt10–<sup>15</sup> < 0.167\*L LGPt<5 = 0 LGPt10–<sup>15</sup> < 0.250\*L LGPt<5 = 0

LGPt10–<sup>15</sup> < 0.500\*L LGPt10–<sup>15</sup> < 0.667\*L LGPt20–<sup>25</sup> < 0.333\*L LGPt25–<sup>30</sup> < 0.500\*L

LGPt<5 = 0 LGPt10–<sup>15</sup> < 0.200\*L LGPt<5 = 0 LGPt10–<sup>15</sup> < 0.500\*L LGPt10–<sup>15</sup> < 0.500\*L LGPt20–<sup>25</sup> < 0.333\*L LGPt25–<sup>30</sup> < 0.333\*L

Crop Grain Grain Grain Silage

Sub-optimum conditions LGPt < 10 = 0 LGPt > 25 = 0 LGPt<5 = 0 LGPt > 30 = 0

Optimum conditions LGPt < 15 = 0 LGPt > 25 = 0 LGPt<5 = 0 LGPt > 30 = 0

Table 1. Corn growing period L (LGPt5), optimum, and sub-optimum conditions of tropical lowland, tropical highland, and subtropical and temperature cultivars for grain production, as well as subtropical and temperate cultivars for silage

growing degree days.

ET ≤ 0.5 \* reference ET.

Growing period L (LGPt5) (days)

Adapted and simplified from [66]

production.

There have been efforts to combine observations with crop simulation models to evaluate changes in yield and yield stability. Leng [61] found yield variability across the US Corn Belt has decreased from 1980 to 2010 with climatic variability the major factor affected variability among years and regions. He found that statistical models explained more of the yield variation than crop simulation models. Bhattarai et al. [62] used the Environmental Policy Integrated Climate (EPIC) model with the combined results for eight general circulation models to show that under low and medium carbon scenarios, corn yields during the period 2080–2099 increased compared to the 2015–2034 period, while under the high carbon scenario yields during these same periods decreased. Lychuk et al. [63]) also used EPIC for the southeastern United States and found in the near-term corn yields increased, but from 2066 to 2070 yields decreased 5–13% because of the increased temperature stress. Huang et al. [64] combined field experiments with crop simulation models to evaluate the potential effect of different growing season length corn hybrids and found the longer growing season hybrid did not yield as high as the medium length hybrid. These results suggest that efforts be placed in evaluating the efficiency of plant growth relative to the changes in temperature and the accumulation of growing degree days.

6. Simulation models to quantify climate effects

106 Corn - Production and Human Health in Changing Climate

provide food security for the future population growth.

Simulation models have been extensively used to estimate the impact of a changing climate on productivity. In 2014, Challinor et al. [27] summarized 1700 published reports using simulation models and the number of papers has increased rapidly since that time. Simulation models provide the capability of assessing the potential impacts of the change in temperature and precipitation under a given CO2 regime and often models using the different emission scenarios to determine the expected temperature and precipitation parameters which are then placed into crop simulation models [54, 55]. It has been found that an ensemble of crop models provides a more rigorous approach to estimating crop responses to climate. This is being conducted under the Agricultural Model Implementation and Improvement Project (AgMIP) framework as described by Rosenzweig et al. [56]. Bassu et al. [57] used this framework to compare 23 different corn models and found temperature decreased yield by approximately 0.5 Mg ha<sup>1</sup><sup>C</sup><sup>1</sup> while doubling the CO2 from 360 to 720 <sup>μ</sup>mol mol<sup>1</sup> increased yield by 7.5% across all models and sites. They concluded that temperature increases would be the dominant factor affecting corn yields. Zhao et al. [58] summarized a number of published results and found for each 1C increase, corn yields decreased by 7.4%. Jin et al. [59] used the Agricultural Production Systems sIMulator (APSIM) model to evaluate the effect of different CO2 scenarios (RCP4.5 and RCP8.5) for corn production in the US and found drought will be the largest factor affecting production. However, they stated that combined impacts of temperature and water stress need to be evaluated in breeding programs and adaptation strategies [59]. Earlier, Jin et al. [60] evaluated the algorithms in 16 different corn models and concluded that heat and drought stress was best simulated when models used event-based heat and water stress descriptions, accounted for nighttime temperature stresses, and evaluated the interactions of multiple stresses. Crop models allow for an assessment of the role of genetics and management on productivity for a range of present and future environmental conditions. Hatfield and Walthall [31] utilized this concept as the G x E x M (genetics environment management) framework to determine how these interactions would need to be understood to

There have been efforts to combine observations with crop simulation models to evaluate changes in yield and yield stability. Leng [61] found yield variability across the US Corn Belt has decreased from 1980 to 2010 with climatic variability the major factor affected variability among years and regions. He found that statistical models explained more of the yield variation than crop simulation models. Bhattarai et al. [62] used the Environmental Policy Integrated Climate (EPIC) model with the combined results for eight general circulation models to show that under low and medium carbon scenarios, corn yields during the period 2080–2099 increased compared to the 2015–2034 period, while under the high carbon scenario yields during these same periods decreased. Lychuk et al. [63]) also used EPIC for the southeastern United States and found in the near-term corn yields increased, but from 2066 to 2070 yields decreased 5–13% because of the increased temperature stress. Huang et al. [64] combined field experiments with crop simulation models to evaluate the potential effect of different growing season length corn hybrids and found the longer growing season hybrid did not yield as high as the medium length hybrid. These results suggest that efforts be placed in evaluating the The Global Agro-ecological zones model (GAEZ) categorizes areas suitable for crop production by climate, soil, terrain, management, and the specific growth limitations of crops, among others [65, 66]. One essential concept of GAEZ climate module is the temperature growing period (LGPt), where air temperature is used as a proxy to estimate days of the growing period with optimal, sub-optimal, and no suitable crop production conditions for a specific crop. The growing period L is defined as the number of days with average daily temperature > 5C (i.e., LGPt5). The corn-specific LGPt's are summarized in Table 1. For example, assume a temperate corn cultivar for grain production with a total growing period between 90 and 180 days. During this period average daily air temperature shall not decrease below 5C, and the number of days with daily average air temperature between 10 and 15C shall be below ⅟<sup>5</sup> of the total growing period to reach optimum growing conditions. In addition to air temperature, the length of the growing period is further limited by the moisture regime, defined as actual ET ≤ 0.5 \* reference ET.

The GAEZ model also estimates potential yield of a specific crop in a specific agro-ecological zone, and applies constraint factors, such as heat or water stress, to calculate actual yield and yield gap. For example, periods of potential water stress occur when actual ET is below the total water requirement of a crop, maximum ET, and the difference between both cannot be compensated by precipitation, plant available water, or irrigation. Maximum ET is calculated as reference ET multiplied by crop coefficient kc. Maximum ET is crop specific and changes during crop development by applying crop-development specific kc values (Figure 6). The derived water stress data is then used to calculate yield constraining factors. The GAEZ model


Table 1. Corn growing period L (LGPt5), optimum, and sub-optimum conditions of tropical lowland, tropical highland, and subtropical and temperature cultivars for grain production, as well as subtropical and temperate cultivars for silage production.

temperatures during the pollination and grain-filling stages. The largest impact on corn production will remain linked to the availability of soil water through precipitation and variation in precipitation during the grain-filling period will have the most detrimental impact on corn production. To overcome the effects of climate change there will be shifts in areas where corn is produced; however, these shifts may not be into areas with the capacity of the soil to support high production or have large variation in yield among years due to the variation in within season weather [33]. What will be critical is to increase our understanding of the G E M interactions as suggested by Hatfield and Walthall [31] in order to reduce the risk in production from a changing climate. What will be critical will be to use our current knowledge base (i.e. genetic resources (G) and management techniques (M)) to determine the viability of potential adaptation strategies to overcome climate changes (E). Combining experimental studies with crop simulation models will advance our understanding of the complex interactions occurring between the biological system and the physical environment and guide us toward viable adaptation practices with the potential to offset the negative impacts of climate change.

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USDA-ARS National Laboratory for Agriculture and the Environment, Ames, Iowa, USA

[1] Collins M, Knutti R, Arblaster J, Dufresne J-L, Fichefet T, Friedlingstein P, Gao X, Gutowski WJ, Johns T, Krinner G, Shongwe M, Tebaldi C, Weaver AJ, Wehner M. Longterm climate change: Projections, commitments and irreversibility. In: Stocker TF, Qin D, Plattner G-K, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midgley PM, editors. Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, United Kingdom and New York, NY, USA: Cambridge University Press; 2013

[2] Trenberth KE. Changes in precipitation with climate change. Climate Research. 2011;47:

[3] Hatfield JL, Boote KJ, Kimball BA, Ziska LH, Izaurralde RC, Ort D, Thomson AM, Wolfe DW. Climate impacts on agriculture: Implications for crop production. Agronomy Jour-

[4] Leakey ADB, Uribelarrea M, Ainsworth EA, Naidu SL, Rogers A, Ort DR, Long SP. Photosynthesis, productivity, and yield of maize are not affected by open-air elevation of

CO2 concentration in the absence of drought. Plant Physiology. 2006;140:779-790

Author details

References

123-138

nal. 2011;103:351-370

Jerry L. Hatfield\* and Christian Dold

\*Address all correspondence to: jerry.hatfield@ars.usda.gov

Figure 6. Crop development specific kc values for corn: kc1, kc2, kc3, and kc4 applies for the initial (d1), vegetative (d2), reproductive (d3), and maturation (d4) development period, respectively. Crop coefficient kc5 applies to the end of the growing period. Corn kc2 and kc4 data are linearly interpolated between kc1, kc3, and kc5. The four corn development stages make up 15, 30, 35, and 20% of the total growing period. Data, equations, and redrawn graph according to IIASA/ FAO [66]. In this example, total growing period (day of planting until harvest) was 173 days, for two corn fields nearby Ames, IA, USA from 2006 to 2017.

also determines which production areas are threatened by climatic changes by applying different climatic scenarios. Using this approach, Teixeira et al. [67] estimated that 5 Mha of cropland suitable for corn production are at risk due to climate change induced heat stress, and that yield declines are expected especially in the Northern hemisphere between 40 and 60N latitudes.

One of the large challenges and opportunities for simulation models will be to incorporate the expected changes in insect and disease populations affecting corn production and link this with the production models. Integration of these two aspects into a single framework will allow for a more complete assessment of the corn production system being experienced by producers.

#### 7. Conclusions

Climate impacts on corn production due to the changing temperature and precipitation regimes in the corn growing areas. The largest impact of these changes will be at the local scale where within season weather induced by the change in climate will become more noticeable. Increasing temperatures will increase the rate of phenological development during the vegetative and reproductive stages; however, the most negative effects will be exposure to high temperatures during the pollination and grain-filling stages. The largest impact on corn production will remain linked to the availability of soil water through precipitation and variation in precipitation during the grain-filling period will have the most detrimental impact on corn production. To overcome the effects of climate change there will be shifts in areas where corn is produced; however, these shifts may not be into areas with the capacity of the soil to support high production or have large variation in yield among years due to the variation in within season weather [33]. What will be critical is to increase our understanding of the G E M interactions as suggested by Hatfield and Walthall [31] in order to reduce the risk in production from a changing climate. What will be critical will be to use our current knowledge base (i.e. genetic resources (G) and management techniques (M)) to determine the viability of potential adaptation strategies to overcome climate changes (E). Combining experimental studies with crop simulation models will advance our understanding of the complex interactions occurring between the biological system and the physical environment and guide us toward viable adaptation practices with the potential to offset the negative impacts of climate change.
