CLIMATE CHANGE SCENARIOS IN THE MIDWESTEN, US
This paper is in preparation to be submitted to Global Change Biology. Mitchell E. Baum, Mark A. Licht, Isaiah L. Huber, and Sotirios V. Archontoulis
Abstract
The choices of planting date and hybrid maturity length determine the yield potential of any crop at any region and thus have substantial economic implications. Research on how those management choices change across temporal and spatial scales and climate change scenarios is rare. This study examines this issue for Iowa, the leading US state in maize (Zea
mays L.) production by using a well-calibrated crop model (APSIM) to determine optimum
planting dates and optimum planting date windows for eight maize hybrid relative maturities (90–118 d) under current and future weather scenarios. Across 36 years and 500 fields, we found that inter-annual variability in historical weather (CV of 50.9%) causes a larger variation in the optimum planting window than the projected weather scenarios (CV of 14.4%). Under a 1ºC increase in mean annual temperature the growing season duration increased 9.8 days while the optimum planting date changes by -2 to +4 days depending on hybrid maturity. Under a more realistic weather scenario (increase minimum temperature by
0.5oC, decrease in maximum temperature by 0.5oC, increase spring rainfall by 10% and
decrease summer rainfall by 10%) the optimum planting date did not change compared to current trends but the yield increased by 6.6%. We concluded that more efforts should be placed in developing dynamic (year-by-year) recommendation tools to inform farmers’ with
choices of optimum planting date ad hybrid maturity to increase profitability and environmental sustainability of Midwestern, US.
Introduction
Maize (Zea mays L.) grain production in the Midwestern US contributes more than $28 billion per year to the US economy (Hatfield et al., 2017). Previous studies have shown that climate change is effecting the temperature and precipitation in this region (Pan et al., 2004; Brown and Degaetano, 2013; Arritt, 2016) and thus the length of the available growing season and yield potential (Wuebbles and Hayhoe, 2003). It is important to understand how each of these changes in climate impact crop yields and more importantly what can be done in terms of management to mitigate these changes or adapt to them.
Planting date and hybrid maturity are the two most important management decisions determining yield potential. Yet, the analysis of climate change impacts on corn yield assume one hybrid and one planting date. Analysis of current year-to-year variability has received less attention compared to future climates. Yield potential is theoretically maximized by an early planting of a full season relative maturity (Richards, 1996). Full season relative
maturity can more fully exploit a longer growing season with greater utilization of radiation, water and nutrients (Andrarde et al., 1992, Parker et al., 2016; Tsimba et al., 2013). Nutrient and water stress during the anthesis silking interval can lower yields regardless of planting date or hybrid relative maturity (Edmeades et al., 2000).
In Iowa, US, grain yields are trending up at a rate of 0.135 Mg ha-1 yr-1 with 53% of the increase being attributed to earlier planting dates from 1979 to 2005 (Kucharik, 2008). The mean planting date over a 24-year period has advanced 0.4 d yr-1 in the Midwestern US (Sacks and Kucharik, 2011). Without appropriate analysis, it is not clear whether the earlier
planting date of maize in Iowa is due to weather (e.g. warmer spring; Wuebbles and Hayhoe, 2003) or technology advancements (e.g. equipment, seed vigor; cold tolerance; Kucharik, 2008).
Precipitation patterns are becoming more unpredictable as inter-annual variation ranges from +100% to -40% mean deviation for a given year. These trends are shifting to more total spring precipitation and increasing intensity of precipitation events (Hatfield et al., 2014). However, the amount of annual precipitation is not predicted to increase, suggesting less precipitation during the summer months.
Daily summer temperatures are increasing across the Midwestern US due to increasing daily minimum temperatures (Brown and Degaetano, 2013). With increasing temperature effecting the amount and timing of rainfall events throughout the year (Arritt, 2016). Warmer daily temperatures are expected to accelerate crop growth and development, therefore, leading to a reduction in the duration of active crop growth. By simulating the effects of increased temperature on grain yield across Northeast China, Liu et al. (2013) found a 1ºC temperature increase across the growing season would reduce grain yield by 4– 22% when planting date and relative maturity selection were kept constant. However, adjusting management practices to planting earlier with a full season relative maturity increased yield by 4% and 13–38% respectively. There is a trade off with increasing daily minimum temperature post silking: increased minimum temperatures force a shorter grain fill period by accelerating phenology, but on the other hand, leads to the crop reaching maturity in a timely manner to avoid damage by early frost (Liu et al., 2012).
Climate directly effects crop growth, yield and, ultimately, farmer profits. It is important to understand how a changing climate may impact maize growth in the
Midwestern US and in doing so, determine the optimum planting date and relative maturity for a changing climate. As of today, there are three types of analyses regarding the optimum planting date: 1) analysis of historical National Agriculture Statistics Service (NASS) data (Kucharik, 2008); 2) field experiments with few site-years (Baum et al., 2018); and 3) analysis of weather trends and assumptions for possible changes in planting dates. Each approach has merits and added new knowledge. Important aspects for decision support are to be developed. Most important is the ability to predict the interactions between planting date, hybrid, weather and geographic location. This establishes a baseline to stimulate further research (e.g. machine learning optimization) and potential solutions.
Crop models are good tools to explore this issue for two reasons. First, crop models integrate various aspects of the soil-crop system and predict emergent G*E*M phenotypes and second, there no experimental field data that covers a large temporal and spatial domain. Crop models have been used to project yields using projected weather scenarios (Jin et al., 2018; Liu and Basso, 2017; Rurinda et al., 2015) but there are rarely crop model applications as part of management decisions. To our knowledge, there are no studies to quantify the temporal and spatial variability of the maize planting date and determination of an optimum planting date window under current and future weather scenarios. This approach can shed light onto the relative maturity by planting date by environment interaction and draw a baseline for decision-making. Here we couple ground-trothed data from the center of the Midwestern US (Baum et al., 2018), a well calibrated field level model (Holzworth et al., 2014) and a parallel system (Elliot et al., 2014) to extrapolate information from few site-year to the entire state of Iowa for a 36-year period. Our objectives were to:
1) Determine the optimum planting date (PD) and PD window, and 2) Determine how the PD window changes in response to variation in climate and potential climate change.
Methods Field scale modeling to calibrate hybrids
The Agricultural Production Systems sIMulation (APSIM; Holzworth et al., 2014) version 7.10 was used to calculate an optimum PD under current and future weather
scenarios. As a systems model, APSIM contains several crop and environmental models. The soil water and nitrogen models of version 7.10 have been extensively validated in Iowa, US and show good performance (Archontoulis et al., 2014; Martinez-Feria et al., 2016; Togliatti et al., 2017; Puntel et al., 2018). In this study, the performance of the new APSIM Maize model included in 7.10 version was calibrated using six modern hybrids with varying relative maturity (95 to 113 days). Calibration of cultivar parameters used data from a recent
experiment carried out in Iowa with seven locations, three years, three planting dates and three hybrids (Figure 1). Experimental details are provided in Baum et al. (2018), the
approach to calibrate the model is included in supplemental materials, the derived parameter values in Supplemental table 1 and model performance before and after calibration in Figure 1. Model performance was determined using the XY plot slope, coefficient of determination (R2) and relative mean square error (RMSE; see data analysis section). Verification of APSIM (Figure 1) at a location level showed acceptable agreement between the APSIM Maize model and field studies conducted by Baum et al. (2018) on grain yield. The RMSE of the combined cultivars and location was found to be 2.18 Mg ha-1 and R2 was 0.72 of the 1:1 line.
To determine the effect of climate variation on optimum PD per relative maturity (RM) and per location along with spring and fall frost dates, mean temperature (°C) and total precipitation (mm) were gathered for each location from the Iowa Environmental Mesonet (IEM, 2016). The difference between the last spring frost and first fall frost was used to determine the growing season duration. Frost dates were realized when ambient air temperatures were at or fell below 0°C.
Regional scale modeling using APSIM and pSIMS
Regional scale analysis was conducted using the parallel System for Integrating Impact Models and Sectors (pSIMS) framework (Elliot et al., 2014). The pSIMS framework was chosen for its ability to allow for high performance computing simulations that extend over a large spatial extent, over many growing seasons or evaluation of many alternative management practices or input configurations. This makes pSIMS ideal for investigating climate change, vulnerability, impacts and potential adaptations on crop growth (Elliot et al.,
2014). The pSIMS framework was set to run for 36 years from 1980–2015 at a 10 km2
resolution. To estimate the optimum PD, PD window and yields at the optimum PD, pSIMS simulations were ran across planting dates ranging from 15 April to 15 June, once every 15 days. This resulted in 1,144,040 simulations.
Scenario modeling
Although weather variation within a 36-year period is captured, weather impacts on the optimum PD, duration of the PD window and crop yields were explored using four weather scenarios to assist decision-making. By utilizing the Representative Concentration Pathway weather scenario, it is difficult to dissect possible changes due to temperature and precipitation. To analyze how temperature and precipitation we created six climate scenarios, we mimicked the approach taken by Liu et al (2012). Do so we either manipulated a single
variable taken from baseline values for either temperature, precipitation or a combination of the two per climate scenario. Baseline, we used the climate provided by Iowa Environmental Mesonet for 1980–2015. Doing so six climate scenarios where created. Scenarios 1 through 3 were aimed at studied the impact of increasing daily temperature, doing so we simulations were run using a 1, 2, and 3°C increase on the daily mean temperature. Scenario 4 was set to look at potential shifts in precipitation as a 10% increase amount of April, May and June precipitation and a 10% decrease in July, August and September precipitation (Wuebbles and Hayhoe, 2003). Scenario 5 has a 0.5°C decrease in maximum temperature and 0.5°C increase in minimum temperature to simulate a lower vapor presser deficit (Brown and Degaetano, 2013), and the final scenario is the combination of scenarios 4 and 5 (Liu and Basso, 2017). Throughout this study, changes to atmospheric carbon dioxide were not considered because C4 crops, such as maize, are not expected to be effected by elevated levels of carbon dioxide (Pugh et al., 2016).
A factorial simulation design was set up to determine optimum PD changes per RM and per year as well as the potential frost susceptibility associated with planting outside of the PD window. Two new RM were added to the existing dataset, a lower and higher relative maturity of 90 and 118 day maturities respectively. There were 17 planting dates used
ranging from 5 April (DOY= 95) to 30 June (DOY = 181). The cultivar parameter values are provided in supplemental materials. In short, cultivars were developed by extrapolating information from calibrated hybrids since there were strong correlations between parameter values and RM (see supplemental materials). Relative maturity values were taken from a list of adapted ranges acceptable for Iowa (Iowa Crop Improvement Association, 2018). As RM ratings shared similar results they were grouped into 3 categories: short, medium and long
season. The short season category contained RM 90 and 95, the medium season category contained 104, 106 and 109. And the long season group contained RM 111, 113 and 118.
Frost susceptibility can be a major hindrance to crop growth. Frost susceptibility was calculated as the percent of simulations effected by frost damage as defined by minimum air temperatures below 0ºC. Spring frost was calculated by the percent of simulations that emerged but failed to accumulate biomass. A fall frost was calculated by the percent of simulations that were harvested without reaching physiological maturity. APSIM will not harvest crops unless they reach physiological maturity. In the case of the crop succumbing to a fall frost, APSIM was automatically set to harvest 15 November regardless of crop stage. The 15 November date was chosen because if the crop had not reached physiological maturity at that time, growth would have either been terminated by fall frost or further crop progress will be limited by low temperature and radiation.
Data and statistical analysis
An optimum PD was determined by fitting a simple quadratic regression model with yield as the response variable and planting DOY as the explanatory variable by latitude, longitude and RM combination. The optimum PD equals the DOY that represents the vertex of the curve. A PD window was calculated by finding the difference between the two points where yield was 98% of the vertex. The pSIMS framework was also used to generate statewide maps for last spring frost date, first fall frost date, mean monthly maximum and
minimum temperatures and total monthly precipitation. The R2 was used to calculate model
performance (Supplemental Figure 10).
An analysis of variance (ANOVA) was used to determine treatment effects on the simulated data. The ANOVA was derived using R software (R Core Team, 2017). The model analyzed the interaction among study factors (location, year, planting date and RM) on grain
yields. Replication across years and treatments within a location were considered random effects to derive the standard deviation of the mean for each treatment. A least significant
difference test at alpha 0.05 was conducted for mean separation analysis. The RMSE and R2
were used to calculate model validation of predicted versus observed variables (Archontoulis and Miguez, 2015). Model validation parameters were calculated using the caret package (Kuhn, 2018). Variability within model treatments was measured using the coefficient of variation (CV) equation [1]
[1] CV = σ/μ*100
where σ is the standard deviation for the treatment variable and μ is the mean of the treatment variable.
Economic analysis of climate scenarios
Iowa is consistently amongst the top producers of maize in US. Contributing 17.84% of the national maize production in 2017 (USDA, 2018). An economic analysis of the potential climate change scenarios was restricted to the state of Iowa. As Iowa contributes nearly one fifth of the national production and located in the center of the US Corn Belt. Economic impact was determined by taking the state wide mean economic impact per scenario and per cultivar group. Economic impact was calculated by
[2] 𝐸𝑐𝑜𝑛𝑜𝑚𝑖𝑐 𝐼𝑚𝑝𝑎𝑐𝑡 = 𝑌𝑖𝑒𝑙𝑑 𝑥 𝐻𝑎𝑟𝑣𝑒𝑠𝑡 𝑎𝑐𝑟𝑒𝑠 𝑥 𝐺𝑟𝑎𝑖𝑛 𝑃𝑟𝑖𝑐𝑒
where yield is the simulated yield on the optimum PD, Harvested acres are the mean state wide harvested acres between the years of 1980 through 2015 (NASS data), and grain prices were calculated using the mean price over our years of study, 1980 through 2015, the
Results
Long season RM had an optimum PD of 23 April (sd = 6.1 days) whereas short season RM had an optimum PD of 10 May (sd = 2.0 days). The mean PD window decreased from 14 to 11 days as RM increased from short to long season (Figure 2). The optimum PD for simulations at baseline conditions differ markedly among selected RM (Figure 3b). With variability around the optimum PD increasing with increasing RM selection. The longest season RM had a CV of 6% around the optimum PD, compared to the shortest season RM having a CV of 2%. Eluding to year-to-year stability of optimum PD being negatively correlated to increasing RM. While higher RM have an earlier optimum PD than shorter season RM. An earlier planting time doesn’t always correlate to higher yields (Figure 3a). For long and medium season RM northern Iowa is outside of their respective adopted range. As such yield within northern Iowa is often lower for long and medium RM, when compared to short season RM. State wide yields for short season RM are often higher than medium and long season RM, as they are well suited to grow within the entire state. However when planted within their respective adoptive region both medium and long season RM produce greater yields.
Location within the state significantly affected the length of the PD window (Figure 3c). Regardless of RM and location, temporal variation had the largest influence on the optimum PD and PD window with a coefficient of variation (CV) of 8% and 51%
respectively. Using RM 109 as an example of the large inter-annual variability, the optimum PD in Iowa can be between 1 April (DOY 91) and 31 May (DOY 151) with CV ranging from 3 to 11% (Figure 4). The inter-annual variability around the optimum PD of the other RM ranged from 0.6 to 15% (Supplemental Figures 1 – 7). This variability exists as the amount of frost free days or active growing season is heavily influenced by year and latitudinal positon
in the state (Supplemental Figure 8). However the mean amount of frost free days in Iowa was 168 days with a standard deviation of 16 days (Figure 5). Across years, the southern locations had a 26-day larger interval of frost free days than the northern locations (Table 2). This interval decreased at a rate of 6.9 days per degree latitude increase, from south to north
(R2 = 0.95). However an analysis of the amount of frost free days per decades indicated that
the mean planting date in Iowa has shifted earlier. The timing between last spring and first fall frost has increased on average 9 days since the 1992 to 2015. Over the same time frame it was found that the increase in the amount of frost free days was almost entirely attributed to warmer falls. The mean first fall frost was found to occur roughly 10 days later in 2015 than in the 1980s, coincidently the timing of the first spring frost was roughly unchanged during this timeframe. This contradicts NASS survey data, showing that the timing of the first 25% planting in Iowa is occurring on average 5 days earlier since the 1980s (Figure 6).
As timing of the first fall frost can either slow or end crop growth if the plant has yet to reach physiological maturity, it is relevant to quantify the risk of how RM selection and planting date to their susceptibility of fall frost damage. Using planting date simulations from APSIM a risk factor was determined by calculating the percent of crops that failed to reach physiological maturity at harvesting per planting date and per RM. It was found that the probability of the crop not reaching physiological maturity increased exponentially when planted after the respective PD window (figure 7). Shorter RM hybrids had 57.7% lower risk than longer season RM when planted after mid-May (Figure 7). In terms of spring frost there was no difference between RM. Planting after 11 May (DOY = 131) likely will have no spring frost risk but planting before 11 May the risk increases exponentially.
Using the baseline simulations as a benchmark, it was determined climate scenarios had the greatest effect on long season RM when considering optimum PD and economic impact (Table 1). Simulations imposing increases of daily mean temperature had a greater effect on optimum PD but not necessarily yield, when compared to simulations that effected rainfall. An increase in daily temperature expands the amount of time between spring and fall frost dates. The state wide mean of frost free days increased by 10.2 days per 1ºC increase in mean daily temperature (Table 2). The lengthening of frost free days shifts the adaptive region of long season RM to the northern part of Iowa. Doing so has showed a large yield benefit of long season RM as the economic impact increased by 570, 690 and 530 million dollars for 1, 2 and 3ºC respectively (Table 1). Medium season RM showed similar trend of increasing yields for 1 and 2ºC but yields decreased at 3ºC. Most likely due to medium season RM’s adopted region already covering the majority of Iowa, indicating that increasing