# Wheat response to a wide range of temperatures, as determined from the Hot Serial Cereal (HSC) Experiment

Michael J. Ottman, Gerard W. Wall, Jeffrey W. White, Bruce A. Kimball, Pierre Martre

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## To cite this version:

Michael J. Ottman, Gerard W. Wall, Jeffrey W. White, Bruce A. Kimball, Pierre Martre. Wheat response to a wide range of temperatures, as determined from the Hot Serial Cereal (HSC) Experiment. Open Data Journal for Agricultural Research, 2018, 4, pp.16-21. 10.18174/odjar.v4i0.15829. hal-02620853

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# Wheat response to a wide range of temperatures, as determined from the Hot Serial Cereal (HSC) Experiment

Bruce A. Kimball<fnref n="1" />*, Jeffrey W. White<fnref n="1" />, Gerard W. Wall<fnref n="1" />, Michael J. Ottman<fnref n="2" /> and Pierre Martre<fnref n="3" />

**Abstract:** Temperatures are warming on a global scale, a phenomenon that likely will affect future crop productivity. Crop growth models are useful tools to predict the likely effects of these global changes on agricultural productivity and to develop strategies to maximize the benefits and minimize the detriments of such changes. However, few such models have been tested at the higher temperatures expected in the future. Therefore, a "Hot Serial Cereal" experiment was conducted on wheat (*Triticum aestivum* L.), the world's foremost food and feed crop, in order to obtain a dataset appropriate for testing the high temperature performance of wheat growth models. The wheat (*Cereal*) was planted serially (*Serial*) about every six weeks for over two years at Maricopa, Arizona, USA, which experiences the whole range of temperatures at which plants grow on Earth. In addition, on six planting dates infrared heaters in a T-FACE (temperature free-air controlled enhancement) system (*Hot*) were deployed over one-third of the plots to warm the wheat by additional target 1.5°C during daytime and 3.0°C at night. Achieved average degrees of warming were 1.3 and 2.7°C for day and night. Overall, a dataset covering 27 differently treated wheat crops with three replicates each was obtained covering an air temperature range from -2 to 42°C. Herein, the management, soils, weather, physiology, phenology, growth, yield, quality, and other data are presented.

**Keywords:** wheat, global warming, temperature, infrared warming, climate change, planting date.

**1 OBJECTIVE:** The primary objective was to obtain a dataset on the response of wheat to a wide range of temperatures. This was achieved by planting the wheat serially about every six weeks for over two years at Maricopa, Arizona, which experiences temperatures from below freezing to higher than most places on Earth where wheat is grown. In addition, on six of the planting dates, infrared heaters in a temperature free-air controlled enhancement (T-FACE) system were deployed so as to provide a treatment wherein only the crop temperature was varied with respect to reference and control treatments.

**2 FIELD EXPERIMENTS:** Approximately every six weeks for over two years starting in March, 2007, the bread wheat (*Triticum aestivum* L.) cultivar Yecora Rojo (Qualset et al., 1985) was planted in a Hot Serial Cereal (HSC) experiment conducted at Maricopa, Arizona, USA. There were a total of 15 planting dates For six of the plantings (early fall, midwinter, and spring), infrared heater arrays (T-FACE) were deployed in a Latin square experimental design with three replicates each of Heated plots, Reference plots with dummy heaters (i.e., the same housing as real heaters, but no heating elements), and Control plots with no experimental apparatus. As described by Kimball et al. (2008), the plots were 3 m in diameter, and calibrated infrared thermometers were used to measure canopy temperatures in the Heated and Reference plots. The canopy temperature data were processed by dataloggers, which provided 0-10 V control signals to dimmers, which in turn modulated the output of the heaters {[Model FTE-1000 (1000 W, 240 V, 245 mm long x 60 mm wide)] mounted in reflector housings, so as to maintain the canopy temperatures of the Heated plots at 1.5°C warmer than those of the Reference plots during daytime and 3.0°C warmer at night. Additional experimental details are presented in Wall et al. (2011, 2013), Ottman et al. (2012), White et al. (2011, 2012), and Kimball et al. (2012, 2015, 2016).

Solar radiation, air temperature, and wind speed were measured on a weather mast in the experimental field most of the time starting with the fall, 2007 planting. For times when field mast data were not available, we utilized data from the AZMET weather station (http://ag.arizona.edu/AZMET/) located about 1 km away.

<footnote n="1">¹ U.S. Arid-Land Agricultural Research Center, USDA, Agricultural Research Service, 21881 North Cardon Lane, Maricopa, Arizona, 85238, USA.</footnote>

<footnote n="2">² Plant Sciences Department, University of Arizona, Tucson, Arizona 85721, USA.</footnote>

<footnote n="3">³ UMR LEPSE, INRA, Montpellier SupAgro, 34 060, Montpellier, France.</footnote>

<footnote>* e-mail: Bruce.Kimball@ars.usda.gov</footnote>
<table><caption>**Figure 1.** Plot nomenclature diagram for the Hot Serial Cereal Experiment. The H, R, and C designate Heated, Reference (dummy heaters), and Control (no apparatus) plots, respectively, with adjacent numbers indicating replicate number and the underneath numbers being plot numbers. Each S designates a strip of 3 plots (north-south) that were drip irrigated together. Planting dates for the various strips are listed at the top with the sequence numbers in parentheses. For the cases of 19 April, 12 June, and 25 July 2007 plantings, all the plots were C plots. Poor germination in Strip S9 for the 17 September 2007 planting required that Plots C3, H3, and R3 be relocated to nearby Strip S7, and they have been designated as in Strip 7b. Strips S4, S5, and S6 were used for un-heated planting date treatments in 2007, and then in 2008, they were used for two infrared heater experiments. (Updated from Wall et al., 2011).</caption><thead><tr><th rowspan="2">North</th><th colspan="3">17 Sep. 2007 (5)</th><th colspan="3">10 Mar. 2008 (9)<br>1 Dec. 2008 (14)</th><th colspan="3">13 Mar. 2007 (1)<br>2 Jan. 2008 (7)<br>29 Sep. 2008 (12)</th><th rowspan="2">13<br>Feb.<br>2008<br>(8)</th><th rowspan="2">27<br>Oct.<br>2008<br>(13)</th><th rowspan="2">28<br>Apr.<br>2008<br>(10)<br>25<br>Aug<br>2008<br>(11)<br>12<br>Jan.<br>2009<br>(15)</th><th rowspan="2">Heated<br>Plantings<br>Rep.</th></tr><tr><th>3</th><th>2</th><th>1</th><th>3</th><th>2</th><th>1</th><th>3</th><th>2</th><th>1</th></tr></thead><tbody><tr><td>30<br>Oct.<br>2007<br>(6)</td><td></td><td></td><td></td><td>25<br>Jul.<br>2007<br>(4)</td><td>12<br>Jun.<br>2007<br>(3)</td><td>19<br>Apr.<br>2007<br>(2)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>C1<br>31</td><td>R3<br>28</td><td>H2<br>25</td><td>C1<br>22</td><td>C1<br>19</td><td>C1<br>16</td><td>C1<br>13</td><td>C3<br>10</td><td>R2<br>7</td><td>H1<br>4</td><td>C1<br>1</td><td>C1<br>-2</td><td>1</td><td></td></tr><tr><td>C2<br>32</td><td>C3<br>29</td><td>R2<br>26</td><td>H1<br>23</td><td>C2<br>20</td><td>C2<br>17</td><td>C2<br>14</td><td>H3<br>11</td><td>C2<br>8</td><td>R1<br>5</td><td>C2<br>2</td><td>C2<br>-1</td><td>2</td><td></td></tr><tr><td>C3<br>33</td><td>H3<br>30</td><td>C2<br>27</td><td>R1<br>24</td><td>C3<br>21</td><td>C3<br>18</td><td>C3<br>15</td><td>R3<br>12</td><td>H2<br>9</td><td>C1<br>6</td><td>C3<br>3</td><td>C3<br>0</td><td>3</td><td></td></tr><tr><td>S10</td><td>S9<br>(7b)</td><td>S8</td><td>S7</td><td>S6</td><td>S5</td><td>S4</td><td>S3</td><td>S2</td><td>S1</td><td>S0</td><td>ST</td><td>Strip ID</td><td></td></tr></tbody></table>

A bias between air temperatures measured at the field and by AZMET was detected when data from times when both were operating were plotted against each other. Consequently, AZMET data were adjusted to the field condition using a regression equation that had solar radiation and wind speed as covariates.

To ensure that the dataset could be used to evaluate crop growth models, careful records were kept of agronomic operations, such as tillage; planting and emergence dates; amounts, dates, and types of fertilizer applications; and dates and amounts of irrigations. Dual meters were used for the irrigations that were applied via drip tubing. Large initial irrigations were applied after each planting, and then an irrigation management program based on estimated evapotranspiration adjusted by measurements of normalized difference vegetation index (NDVI; Hunsaker et al., 2007) was used to schedule weekly (bi-weekly during mid-winter) replacement of potential evapotranspiration from the un-heated Control plots (with adjustments for rainfall). As suggested by Kimball (2005, 2011), we provided supplemental irrigations to the Heated plots in amounts of about 10% more than corresponding Control plots to minimize the effects of increased leaf to air vapor pressure gradients and thereby make the T-FACE treatment more like global warming with constant relative humidity. Additional sprinkling of the summer-planted crops was done to aid germination and emergence.

Biomass of various above-ground organs was sampled at three intervals per crop, and final grain yield and its components were obtained at maturity (Ottman et al., 2012). Leaf appearance rates and phenology were observed (White et al., 2011, 2012). Reflectance in four wave bands was measured
two to five times per week, enabling vegetation indices to be determined (Kimball et al., 2012). Net photosynthesis, stomatal conductance, and plant water status were measured one to two times per crop in Heated and Reference plots (Wall et al., 2011). Soil respiration and soil temperatures were also measured on two planting dates (Wall et al., 2013).

Several quality attributes of mature grains from all planting dates that produced grains and supplemental heat treatments were also measured. Flour total carbon and nitrogen concentrations were determined by the Dumas combustion method (AOAC method no.7.024), and flour total grain protein concentration (AACC method 39-70A; AACC, 1995) and grain hardness (AACC method 39-10; AACC, 1995) were determined by near infrared reflectance spectroscopy (NIRS). The viscosity of non-starch polysaccharides, mainly arabinoxylans, was measured following Bordes et al. (2008). The Chopin Alveograph test was used to assess dough strength, tenacity, extensibility and dough swelling (AFNOR NF ISO 5530-4). The behavior of dough constituents (starch, protein, water) was analyzed by using a Mixolab apparatus (ICC Standard Method No. 173; ICC, 2010). Gliadin classes and glutenin subunits were separated and quantified by reverse-phase high-performance-liquid-chromatography (RP-HPLC) as described in Plessis et al. (2013). Starch granules were extracted following Bancel et al. (2010) and the volume percentages of A- (diameter > 10 µm), B- (2 µm < diameter < 10 µm), and C- (diameter < 2 µm) granules were determined by using a laser beam scattering apparatus (Debiton et al., 2011). The size distribution and radii of glutenin polymers was analyzed by asymmetric flow field-flow fractionation (AF4) coupled with a multi angle laser light scattering (MALLS) detector (Lemelin et al., 2005). Flour starch content was determined as described by Hendriks et al. (2003). The mass percentage, average weight, average molar mass and z-average root-mean-square radii of amylose and amylopectine polymers were analyzed by AF4 coupled with multi-angle light scattering and refractive index detectors (MALS-RI; Chiaramonte et al., 2012).

**3 PRIOR WHEAT GROWTH MODELLING ACTIVITIES:** The HSC dataset has been utilized in some prior model evaluation and inter-comparison activities. The first was by Grant et al. (2011) who found good agreement of hourly canopy temperatures as well as with grain yields with the ecosys model. Kimball et al. (2015) used the data to validate a model predicting the infrared heater requirements needed to conduct T-FACE experiments. However, the largest effort was done by the Wheat Team (http://www.agmip.org/wheat/) of the Agricultural Model Inter-comparison and Improvement Project (AGMIP, Rosenzweig et al., 2013), who did a major inter-comparison study among 30 wheat models with the HSC data (Asseng et al., 2015), as well as predicting likely impacts of global warming on global wheat productivity. The Wheat Team also used the HSC data to improve the accuracy of their responses to high temperature. With 15 of such improved models, Maiorano et al. (2016) showed that both accuracy and precision were indeed improved when tested against several independent datasets from around the world. To evaluate whether simulation of canopy temperature improves the ability of simulate heat stress impacts, nine wheat models were tested against the HSC dataset by Webber et al. (2017). The model output and the HSC data contributing to Asseng et al. (2015) and Maiorano et al. (2017) are presented in a companion paper (Martre et al., 2018).

**4 DATA FORMAT AND STRUCTURE:** A list of the files in the HSC dataset is given in Table 1. The data from the many crops were assembled and formatted following ICASA Version 2.0 standards (White et al., 2013), and they are presented herein. The names of the variables are explained in companion "key" files.

<table><caption>**Table 1.** Overview of the main HSC dataset files.</caption><thead><tr><th colspan="2">Crop and Soil Management Files</th></tr></thead><tbody><tr><td>Crop_management_events.ods</td><td>List of planting dates, cultivar information, plant populations, row spacing, planting depth for all 81 of the planting date and warming treatments and replicate combinations.</td></tr><tr><td>Experiment_treatment_site_information.ods</td><td>List of all the experimental site characteristics, treatments, and associated weather and soil files for all 81 of the planting date and warming treatments and replicate combinations.</td></tr><tr><td>Fertilizer.ods</td><td>Tabulation of N and P fertilizer applications</td></tr><tr><td>Irrigation.ods</td><td>Tabulation of irrigations</td></tr><tr><td>Soil_description.ods</td><td>Soil classification and characteristics by layer</td></tr><tr><td>Soil_initial_conditions.ods</td><td>Initial water, NH<sub>4</sub>, and NO<sub>3</sub> from a previous study</td></tr></tbody></table>
<table><caption>**Table 1. Continued**</caption><thead><tr><th></th><th></th></tr></thead><tbody><tr><td>Soil_residue.ods</td><td>Residue of straw and roots from previous crop</td></tr><tr><td>Wheat_variety_information.txt</td><td>Characteristics of wheat cultivar Yecora Rojo</td></tr><tr><td colspan="2"><strong>Weather, heater, canopy temperature files</strong></td></tr><tr><td>AZ000604.TXT</td><td>Daily weather file for the Reference and Control plots. The 0604 indicates the data start in 2006 and there are almost 4 years' worth of data extending past the end of the HSC experiments.</td></tr><tr><td>AZ110604.TXT</td><td>Special weather file for models that "grow" the crop using air temperature. It was created using AZ000604.TXT as a base and then adding the difference in canopy temperature between the heated and reference plots to TMAX and TMIN. AZ110604 is for heated plot 4, strip S1, 2007-03-13 planting date.</td></tr><tr><td>AZ120604.TXT</td><td>As above but for heated plot 9, strip S2, 2007-03-13 planting date.</td></tr><tr><td>AZ130604.TXT</td><td>For heated plot 11, strip S3, 2007-03-13 planting.</td></tr><tr><td>AZ270604.TXT</td><td>For heated plot 23, strip S7, 2007-09-17 planting.</td></tr><tr><td>AZ280604.TXT</td><td>For heated plot 25, strip S8, 2007-09-17 planting.</td></tr><tr><td>AZ290604.TXT</td><td>For heated plot 30, strip S9, 2007-09-17 planting.</td></tr><tr><td>AZ310604.TXT</td><td>For heated plot 4, strip S1, 2008-01-02 planting.</td></tr><tr><td>AZ320604.TXT</td><td>For heated plot 9, strip S2, 2008-01-02 planting.</td></tr><tr><td>AZ330604.TXT</td><td>For heated plot 11, strip S3, 2008-01-02 planting.</td></tr><tr><td>AZ440604.TXT</td><td>For heated plot 15, strip S4, 2008-03-10 planting.</td></tr><tr><td>AZ450604.TXT</td><td>For heated plot 16, strip S5, 2008-03-10 planting.</td></tr><tr><td>AZ460604.TXT</td><td>For heated plot 20, strip S6, 2008-03-10 planting.</td></tr><tr><td>AZ510604.TXT</td><td>For heated plot 4, strip S1, 2008-09-29 planting.</td></tr><tr><td>AZ520604.TXT</td><td>For heated plot 9, strip S2, 2008-09-29 planting.</td></tr><tr><td>AZ530604.TXT</td><td>For heated plot 11, strip S3, 2008-09-29 planting.</td></tr><tr><td>AZ640604.TXT</td><td>For heated plot 15, strip S4, 2008-12-01 planting.</td></tr><tr><td>AZ650604.TXT</td><td>For heated plot 16, strip S5, 2008-12-01 planting.</td></tr><tr><td>AZ660604.TXT</td><td>For heated plot 20, strip S6, 2008-12-01 planting.</td></tr><tr><td>ReadMe_Weather_File_Choice.docx</td><td>More detailed explanation of which of the several weather files to use for a particular modeling situation.</td></tr><tr><td>Weather_Canopy_Temp_Heater_hourly.ods</td><td>Hourly weather, canopy temperature, long-wave radiation from heaters</td></tr><tr><td colspan="2"><strong>Crop Response Files</strong></td></tr><tr><td>Biomass_Yield_Area_Phenology.ods</td><td>Biomass, grain yield, leaf and other area indices, growth stages.</td></tr><tr><td>Gas_Exchange_and_Water_Relations.ods</td><td>Leaf photosynthesis, stomatal conductance, and leaf water potentials.</td></tr><tr><td>NDVI_Canopy_Reflectance.ods</td><td>Canopy reflectance and NDVI, which is a measure of greenness.</td></tr><tr><td>Grain_Quality.ods</td><td>Grain nitrogen, protein, starch, and other quality compound concentrations.</td></tr><tr><td>Soil_Moisture_2007Mar-2008Jul.ods</td><td>Soil water content of selected plots at six depths at 15-min intervals from March 2007 through July 2008.</td></tr><tr><td>Soil_Moisture_2008Aug-2009May.ods</td><td>Soil water content of selected plots at six depths at 15-min intervals from August 2008 through May 2009.</td></tr></tbody></table>

**5 ACKNOWLEDGEMENTS:** This work was supported by the U.S. Department of Agriculture, Agricultural Research Service and the University of Arizona, Tucson, Arizona. We acknowledge the helpful cooperation of Dr. Robert Roth and his staff at the University of Arizona, Maricopa Agricultural Center, Maricopa, Arizona, USA. Technical assistance by Charles Blackshear, Matthew Conley, Steven Farnsworth, Justin Laughridge, Laura Olivieri, and Zahra Troeh is appreciated. We also thank Dr. Larbi Rhazi (Institut Polytechnique LaSalle Beauvais, France) for his help with the analysis of protein and starch polymer size distributions, and David Alvarez and Annie Faye (UMR GDEC, INRA Clermont-Ferrand, France) for their technical help with other grain analyses.

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