# Integrating Wheat Canopy Temperatures in Crop System Models

^{1}

^{2}

^{*}

^{†}

## Abstract

**:**

## 1. Introduction

- (I)
- developing empirical models to predict maximum, minimum and mean daily canopy temperatures based on meteorological data and environmental variables available from the output of a crop system model operated at a daily time step and
- (II)
- studying potential effects of using derived canopy temperatures as input temperature in crop system models.

## 2. Material and Methods

#### 2.1. Research Sites and Experimental Set-Up

^{2}, cv. Dekan), 2011 (sowing date: 24 October 2010, sowing density: 300 plants/m

^{2}, cv. Dekan) [30], 2013 (sowing date: 31 October 2012, sowing density: 360 plants/m

^{2}, cv. Batis) and 2014 (sowing date: 2 October 2013, sowing density 300 plants/m

^{2}, cv. Batis). Plants were watered with an overhead boom irrigation system in 2010 and 2011 and by drip irrigation in 2013 and 2014. Three different irrigation treatments (W0 = no irrigation, W1 = medium drought stress, W2 = fully irrigated) with four replications were applied (Table 1). Using the rain-out shelter, drought stress was induced from the beginning of April (after danger of ground frost ceased). Canopy height (CH) and leaf area index (LAI) were measured on a weekly basis (LAI2000 plant canopy analyzer, Li-Cor Inc., Lincoln, NE, USA). Regular observations of the developmental stage were performed using the scale proposed by [31]. Volumetric water content was measured in five depths (weekly: 5 and 35 cm, fortnightly: 65, 85 and 105 cm) using the MiniTrase Time Domain Reflectometry (TDR) System (Soil Moisture Equipment Corp., Santa Barbara, CA, USA). Air temperature (T

_{air}), relative humidity and wind speed were measured at a reference height of 2 m within the plots. Net radiation was estimated using NR (net radiometer) Lite net radiometers (Kipp and Zonen, Delft, The Netherlands) within one plot of each treatment. The canopy temperature was derived from infrared (IR) radiometer measurements (in 2010 and 2011: SI 111 and SI 211 sensors, Apogee Instruments, Logan, USA; in 2013 and 2014: IR120 sensors, Campbell Scientific, Logan, UT, USA). The sensors were placed 0.5 m above the canopy using a nadir-viewing angle [30]. Note that the position of the sensors was shifted at regular intervals to account for changes in the canopy height. Plot size averages 3.5 m × 3.6 m (12.6 m

^{2}). The sensors were operated at a frequency of 10 s, and the data were averaged and logged at an interval of 1, 10 and 15 min in the years 2010 and 2011, 2013 and 2014, respectively. (II) At the Braunschweig research site (“BS”), operated by the Thünen Institute, rainfall averages 599 mm annually and the mean air temperature accounts with 9.2 °C [32]. The soil is a luvisol of a loamy sand texture in the plough horizon, followed by a sand and gravel layer of more than 3 m in size [33]. Soil water content was derived from a Trime Pico 64 TDR system (Imko GmbH, Germany) at 0–20 cm and 20–40 cm depth. In 2013 (sowing date: 29 October 2012, sowing density: 380 plants/m

^{2}, cv. Batis), plants were watered by circle sprinklers and manual irrigation (hand shower). Wind speed, net radiation and air temperature were measured at a station operated by the German Weather Service (DWD) located within the research field. The canopy temperature was measured with an IR 120 sensor (Campbell Scientific) at a frequency of 10 s. Data were logged and averaged using an interval of 10 min. The sensors were placed 0.5 m above the mean canopy height using a nadir-viewing angle. Plot size averages 5 m × 4 m (20 m

^{2}). For this study, meteorological and canopy temperature data were averaged to hourly and daily values.

**Table 1.**Summary of the canopy temperature data set (HS = Hohenschulen research site, BS = Braunschweig research site, W0 = no irrigation, W1 = medium deficit irrigation and W2 = full irrigation, n = number of observations (24 h time step), Rep = number of measurement replications, HS P1 and HS P2 = plots at HS research site, Water supply = sum of irrigation water and precipitation from 03/01 to 07/30 (mm).

Year | Irrigation | Site | n | Rep | Observation Period | Water Supply |
---|---|---|---|---|---|---|

2010 | W0 | HS | W0: 78 | 1 | W0: 05/13–07/30 | 4 |

W1 | W1: 78 | W1: 05/13–07/30 | 177 | |||

W2 | W2: 78 | W2: 05/13–07/30 | 314 | |||

2011 | W0 | HS | W0: 56 | 1 | W0: 05/08–07/11 ^{1} | 31 |

W1 | W1: 82 | W1: 05/08–07/30 | 197 | |||

W2 | W2: 74 | W2: 05/08–07/30 ^{1} | 361 | |||

2013 | W0 | HS | W0: 57 | 2 | W0: 05/24–07/19 | 43 |

W2 | W2: 57 | W2: 05/24–07/19 | 306 | |||

2014 | W0 W2 | HS, BS | W0: 17 (HS) W2: 43 (HS), W2: 65 (BS) | 1 | W0 (HS): 04/19–05/05 | 15 |

W2 (HS, P1): 04/19–05/20 | 295 | |||||

W2 (HS, P2): 04/19–04/30 | 295 | |||||

W2 (BS): 05/16–07/19 | 383 |

^{1}data gap between 06/03 and 06/10.

#### 2.2. Canopy Temperature Data

**Figure 1.**Density plot of available surface temperature observations (Tc, daily averages of hourly values) for the different observation years and irrigation treatments (W0 = no irrigation, W1 = medium deficit irrigation and W2 = full irrigation).

#### 2.3. Crop System Modeling

^{−3}MPa in the top soil layer [42]. Potential transpiration (Tpot) is the difference between the ETP and the sum of interception evaporation and Epot [30]. The soil water uptake by the plants from each layer, expressed as a layer sink term [43], sums up to actual transpiration (Tact). The sink term of each layer is modeled by distributing Tpot to the rooted layers. The partitioning is controlled by the root length in each layer, modified by a root water uptake competition factor [44] and reduced by a layer-specific reduction factor depending on the layer-specific soil water potential [37]. For the model parametrization literature values, measurements and results from an optimization method based on the Levenberg–Marquard algorithm (implemented in the modeling framework) are used. During the model fitting and parametrization, the soil texture was chosen for each plot separately [30] to account for site heterogeneities.

#### 2.4. Statistical Data Analysis and Model Formulation

_{c,min}, T

_{c,mean}and T

_{c,max}). Selected covariates are observed at standard meteorological stations (net radiation (Rn), total incoming radiation (Rint), air temperature (T

_{air}), humidity (RH) and saturation vapor pressure deficit of the air (VPD), rainfall (R) and wind speed (W)) or commonly available from the output of crop system models (as specified in the following). Using the HUME model (cf. Section 2.3), we simulated time series data of crop height and leaf area index and calculated the main components of the plant-water-cycle, such as the total and plant available soil water content, ETA (= Eact + Tact), ETP, Tpot and Tact, Epot and Eact (cf. Section 2.3). We transformed absolute model results to relative values by formulating a range of ratios based on the available possible combinations of these variables, characterizing plant and soil water relations independent from the absolute amount of available water. Different formulations describing the evaporation (Eact/ETP and Eact/ETA, Eact/Epot) and the transpiration (Tact/ETP, Tact/ETA, Tact/Eact, Tact/Tpot) were applied as potential model covariates. Furthermore, we tested the suitability of the interpolated LAI, the natural logarithm of the LAI (LAI

_{log}) and crop height (CH) values as predicting variable and tested for potential interaction effects between modeled environmental (as specified above) and meteorological data.

^{2}) were automatically calculated using a forward stepwise-based selection procedure (FWDselect function [46]). Meteorological, as well as environmental data are generally strongly inter-correlated. We assessed the multicollinearity of the co-variables by using the variance inflation factors (VIF; corvif function [48]) and consider VIF ≤3 as a benchmark [50]. Model reformulation and optimization were based on a quantile regression (QR) analysis (qr function [49]). The quantile regression is based on the conditional quantiles of the response variable distributions, thus, offering a more complete view of possible causal relationships between variables and useful for ecological applications with a limited number of available and strongly interacting variables [51]. Results from the quantile regression showed that the contributions of the selected independent variables to the conditional distribution of the canopy temperatures can vary significantly at different levels of canopy temperatures. Such patterns are hidden in ordinary multiple least-square regression model analyses. The predictive quality of the explanatory variables when applied to the training and testing data was identified at the 5% level of significance (p < 0.05).

#### 2.5. Impact Study

_{c,mean}, T

_{c,min}and T

_{c,max}for a well-irrigated wheat plot (W2) and for a not irrigated wheat plot (W0) are computed using the multiple linear regression models presented in this study (cf. Section 2.4) using data from the HS research site (cf. Section 2.1). Most crop system models apply air temperature-based “heat units” and use temperature thresholds to simulate the impact of temperatures on crop development, crop physiological processes and crop yield. To highlight the potential impact of the differences between air and crop temperatures on corresponding model results, we

- (I)
- calculate cumulative sum curves of the difference between modeled daily canopy temperature and the air temperature (∆T),
- (II)
- compute the number of days where modeled and measured canopy temperatures and air temperatures exceed temperature thresholds of 20 °C, 25 °C and 30 °C,
- (III)
- calculate extreme thermal unit (ETU) sums above the optimal temperature of 20 °C (according to [52]).

## 3. Results and Discussion

#### 3.1. Empirical Models of Daily Canopy Temperatures

_{c,mean}, T

_{c,min}and T

_{c,max}(cf. Section 2.4) as a major result from our study. The relative importance of the covariates for the total estimated R

^{2}is described and discussed in Section 3.3.

_{c,min}and T

_{c,max}) and/or selection of model covariates (models of T

_{c,min}and T

_{c,mean}).

_{c,mean}), the covariates explaining more than 90% of the total variability are the mean daily air temperature (°C), the incoming radiation (Rint (W·m

^{−2})), the natural logarithm of the leaf area index (LAI

_{log}), the vapor pressure deficit (VPD (hPa), only for phase I), the ratio of actual evaporation to ETP (Eact/ETP, only for phase II) and the product of the transpiration ratio, defined as the ratio of actual transpiration to the potential transpiration, and the VPD ((VPD*(Tact/Tpot)), only for phase II). We account for the changing contribution of the VPD, the Eact/ETP ratio and the product of transpiration ratio and VPD by using a dummy variable (DPhen), which equals 1 in the pre-heading and 0 in the post-heading phase (Table 2).

**Table 2.**Statistics of the multiple linear regression model developed using a forward stepwise-based selection procedure and quantile regression analyses for predicting T

_{c,mean}(T

_{c,mean}= mean daily canopy temperature) of a winter wheat canopy; SE = standard error, T

_{air,mean}= mean daily air temperature (°C), Rint = incoming radiation (Wm

^{−2}), LAI

_{log}= natural logarithm of the leaf area index, Dphen = phenology dummy variable with Dphen = 1 during phase I (pre-heading) and Dphen = 0 during phase II (post-heading, cf. Section 3.1), VPD = vapor pressure deficit (hPa), Tact/Tpot = transpiration ratio (Tact = actual and Tpot = potential transpiration (both in mm·d

^{−1})), Eact/ETP = ratio of actual evaporation (Eact (mm·d

^{−1})) to potential evapotranspiration (ETP (mm·d

^{−1})).

Estimate | SE | p-Value | |
---|---|---|---|

I & II | I & II | I & II | |

Intercept | 2.730 | 0.266 | <2e^{−16} |

T_{air,mean} | 0.942 | 0.015 | <2e^{−16} |

Rint | 0.005 | 0.001 | 1.27e^{−13} |

LAI_{log} | −1.358 | 0.082 | <2e^{−16} |

(1-Dphen)*Eact/ETP | −5.491 | 0.953 | 1.87e^{−08} |

Dphen*VPD | −0.263 | 0.023 | <2e^{−16} |

(1-Dphen)*(VPD*(Tact/Tpot)) | −0.299 | 0.033 | <2e^{−16} |

_{c,max}are modeled using Rint, T

_{air,max}, the natural logarithm of the leaf area index and the product of transpiration ratio and VPD. For the pre- and the post-heading phase significant changes in the model coefficients were calculated, thus, two model equations were derived (Table 3). For the pre-heading phase, T

_{c,min}can be approximated using T

_{air,min}and the canopy height (CH (m)). For the post-heading phase, it is simulated using the minimum diurnal air temperature, the VPD and the ratio of actual evaporation to ETP (Table 4).

**Table 3.**Statistics of the multiple linear regression model developed using a forward stepwise-based selection procedure and quantile regression analyses for predicting T

_{c,max}(= maximum daily canopy temperature) of a winter wheat canopy; SE = standard error, T

_{air,max}= maximum daily air temperature (°C), Rint = incoming radiation (Wm

^{−2}), LAI

_{log}= natural logarithm of the leaf area index, VPD = vapor pressure deficit (hPa), Tact/Tpot = transpiration ratio (Tact = actual and Tpot = potential transpiration (both in mm·d

^{−1})).

Estimate | SE | p-Value | ||||
---|---|---|---|---|---|---|

I | II | I | II | I | II | |

Intercept | 4.241 | 4.011 | 0.942 | 0.633 | 1.39e^{−05} | 1.58e^{−09} |

Rint | 0.016 | 0.014 | 0.002 | 0.002 | 1.75e^{−09} | 1.15e^{−15} |

T_{air,max} | 0.922 | 0.888 | 0.055 | 0.029 | <2e^{−16} | <2e^{−16} |

LAI_{log} | −2.816 | −1.847 | 0.340 | 0.176 | 8.59e^{−14} | <2e^{−16} |

VPD*(Tact/Tpot) | −0.447 | −0.623 | 0.113 | 0.063 | 0.000118 | <2e^{−16} |

**Table 4.**Statistics of the multiple linear regression model developed using a forward stepwise-based selection procedure and quantile regression analyses for predicting T

_{c,min}(= minimum daily canopy temperature) of a winter wheat canopy; SE = standard error, T

_{air,min}= minimum daily air temperature (°C), CH = crop height (m), VPD = vapor pressure deficit (hPa), Eact/ETP = ratio of actual evaporation (Eact (mm·d

^{−1})) to potential evapotranspiration (ETP (mm·d

^{−1})).

Estimate | SE | p-Value | ||||
---|---|---|---|---|---|---|

I | II | I | II | I | II | |

Intercept | 1.116 | −0.202 | 0.410 | 0.276 | 0.00729 | 0.466 |

CH | −4.147 | - | 0.546 | - | 3.64e^{−12} | - |

VPD | - | −0.101 | - | 0.019 | - | 2.89e^{−07} |

T_{air,min} | 1.088 | 1.013 | 0.031 | 0.024 | <2e^{−16} | <2e^{−16} |

Eact/ETP | - | −3.158 | - | 0.715 | - | 1.65e^{−05} |

#### 3.2. Variability of Canopy Surface Temperatures

_{c,mean}) range from ~8 °C to 26.5 °C (Figure 1) and daily averaged differences between canopy and air temperatures from −4 °C to +3.8 °C (ΔT

_{mean}) (Figure 3).

**Figure 2.**Scatter plot of mean, maximum and minimum canopy temperatures (T

_{c}) and mean, maximum and minimum air temperatures (T

_{air}) derived from the aggregation of hourly measurement data to daily values for three different irrigation treatments (W0 = no irrigation, W1 = medium deficit irrigation and W2 = full irrigation, r = Pearson’s correlation coefficient). The dashed black line indicates the line of identity.

**Figure 3.**Density plot of the mean daily difference between crop and air temperatures (∆T

_{mean}) for the different observation years and irrigation treatments (W0 = no irrigation, W1 = medium deficit irrigation and W2 = full irrigation).

_{mean}) values of the W0 treatment are clearly shifted towards higher values (Figure 3), indicating decreased stomatal aperture and lower transpiration levels under drought stress. The diurnal range of canopy temperatures measured at W0 plots clearly exceeds the diurnal range of air temperatures (Figure 4).

**Figure 4.**Scatter plot of the daily range of canopy temperatures (T

_{c}, hourly values) and air temperatures (T

_{air}, hourly values) for the complete observation period and for all irrigation treatments (W0 = no irrigation, W1 = medium deficit irrigation and W2 = full irrigation).

#### 3.3. Predictive Ability of the Empirical Canopy Temperature Models

_{c,mean}, 2 °C (Phase I) and 1.5 °C (Phase II) for estimating T

_{c,max}and 1.2 °C (Phase I) and 0.8 °C (Phase II) for estimating T

_{c,min}, and could generalize well to the training data set (Table 5).

_{c,mean}decreases with increasing LAI (Table 2). We calculated a negative slope for the VPD during the pre-heading phase and for the Eact/ETP ratio and the VPD-scaled transpiration ratio during the post-heading phase. The negative effect of the LAI and the VPD can be related to an increased transpiration cooling with increasing evaporative demand of the air and an increasing amount of transpiring plant tissues during the pre-heading stages. After the canopy has reached maximum leaf area and maximum height, during the post-heading stage, environmental conditions are characterized by decreased availability of soil water and increased air temperatures. Thus, for subsequent developmental stages, daily transpiration and evaporation help explain the variability of T

_{c,mean}. An increasing fraction of actual evaporation (Eact) from the soil, e.g., after rain events, decreases the amount of energy available for heating the canopy (sensible heat fluxes). The increased latent heat flux arising from the soil further lowers the near-surface temperature. The negative slope of the VPD-scaled transpiration ratio can be related to the cooling effect of a higher amount of plant available soil water, weighted over the root distribution parameters and scaled with the evaporative demand of the air, on canopy temperatures.

**Table 5.**Results of the multiple linear regression fit for all data and for the different irrigation treatments (W0–W2, cf. Section 2.1) using phenological subsets (I = pre-heading phase, II = post-heading phase) for modeling T

_{c,mean}(mean daily canopy temperature (°C)), T

_{c,max}(maximum daily canopy temperature) and T

_{c,min}(minimum daily canopy temperature). R

^{2}and the root mean squared error (RMSE (°C)) are given for the training (50% of all observations) and the testing data (50% of all observations).

Target Variable | Treatment | Phase | Training | Testing | ||
---|---|---|---|---|---|---|

R^{2} | RMSE | R^{2} | RMSE | |||

T_{c,mean} | All | I & II | 0.95 | 0.78 | 0.94 | 0.81 |

W0 | I & II | 0.97 | 0.64 | 0.95 | 0.72 | |

W1 | I & II | 0.99 | 0.36 | 0.99 | 0.39 | |

W2 | I & II | 0.92 | 0.88 | 0.93 | 0.87 | |

T_{c,max} | All | I | 0.79 | 2.08 | 0.83 | 1.84 |

W0 | I | 0.89 | 1.62 | 0.91 | 1.60 | |

W1 | I | 0.91 | 0.86 | 0.93 | 0.94 | |

W2 | I | 0.74 | 2.29 | 0.72 | 1.90 | |

T_{c,max} | All | II | 0.91 | 1.43 | 0.9 | 1.56 |

W0 | II | 0.93 | 1.48 | 0.92 | 1.49 | |

W1 | II | 0.95 | 0.97 | 0.96 | 0.77 | |

W2 | II | 0.89 | 1.26 | 0.88 | 1.52 | |

T_{c,min} | All | I | 0.9 | 1.04 | 0.86 | 1.19 |

W0 | I | 0.9 | 1.02 | 0.89 | 1.04 | |

W1 | I | 0.92 | 0.73 | 0.75 | 1.00 | |

W2 | I | 0.9 | 1.11 | 0.88 | 1.29 | |

T_{c,min} | All | II | 0.91 | 0.81 | 0.91 | 0.85 |

W0 | II | 0.94 | 0.67 | 0.93 | 0.80 | |

W1 | II | 0.97 | 0.45 | 0.96 | 0.54 | |

W2 | II | 0.89 | 0.89 | 0.88 | 0.95 |

_{c,mean}, for subsequent developmental stages, components of the canopy water balance help explain the variability of T

_{c,min}. At night, transpiration is low or close to zero, thus, the actual evaporation is a suitable model covariate. Night-time or early-morning evaporation is driven by the evaporative demand of the air (VPD), giving reason for using VPD as a model covariate.

**Figure 5.**Scatter plot of the measured and the simulated maximum daily canopy temperatures (T

_{c,max}) for the training and the testing data set during the pre-heading (I) and the post-heading phase (II) for all irrigation treatments (W0 = no irrigation, W1 = medium deficit irrigation and W2 = full irrigation). The dashed gray line shows the line of identity. Equations of the multiple linear regression models and r

^{2}(= coefficient of determination) are given (abbreviations as for Table 2, Table 3 and Table 4).

^{2}of the presented empirical models (Figure 6) highlight the dependence of their contribution on the crop water status: while, for the fully irrigated plots (W2), the air temperature contributes from ~60%–70% up to nearly 100% to the estimated R

^{2}of the multiple linear regression (MLR) model, the contribution decreases to <50% for T

_{c,mean}and T

_{c,max}of the W0 plots. In consequence, the percentage contribution of, e.g., incoming radiation and the VPD to the R

^{2}of the W0 canopy temperature models increases. Consequently, for simulating canopy temperatures under the influence of water deficits, solemnly using air temperature does not provide a suitable proxy for temperatures at the canopy level, and interactions between environmental and meteorological variables need to be incorporated.

#### 3.4. Case Study: Wheat Canopy versus Air Temperature

**Figure 7.**Cumulative sums of canopy to air temperature differences (∆T (°C)) for the drought treatment plot (dotted line) and the irrigated plot (dashed line) using mean daily modeled surface temperatures (∆T = T

_{c,mean}–T

_{air,mean}) in 2010 (

**top left**), 2011 (

**top right**), 2013 (

**bottom left**), 2014 (

**bottom right**) (Hohenschulen research site).

_{air,max}), the number of days exceeding temperature thresholds of 20 °C and 25 °C is close to that of using maximum canopy temperatures from the fully irrigated treatment plot (Table 6). However, the number of days is significantly higher if using T

_{c,max}W0. Note that measured temperatures for the drought treatment plot from 2014 are only available until beginning of May and that there is a data gap of seven days in 2011 (Table 1). Despite large interannual differences in the number of days exceeding sample temperature thresholds, our data highlight the need to account for air-canopy temperature differences in threshold-based modeling approaches.

_{c,mean}W2 and highest values using T

_{c,mean}W0 (Figure 8). Note that starting dates differ for each observation year. Absolute summed differences increase during the wheat flowering and ripening stages. It is well known that the difference between canopy and air temperatures increases at higher levels of crop water stress. Our results, however, suggest significant discrepancies in T

_{air,mean}- and T

_{c,mean}-based ETU calculations not only for the drought treatment but also for the well irrigated plot. These differences can be critical for the simulation of the timing of crop developmental stages.

**Table 6.**Number of days exceeding 20, 25 and 30 °C for modeled maximum daily crop temperature of the drought treatment plot (T

_{c,max}W0) and the irrigated plot (T

_{c,max}W2) and for the measured maximum daily air temperature (T

_{air,max}) in 2010, 2011, 2013 and 2014 (Hohenschulen research site). In parentheses, values calculated for available measured canopy temperatures are given (cf. Table 1).

Variable | Year | Days > 20 °C | Days > 25 °C | Days > 30 °C |
---|---|---|---|---|

T_{c,max} W0 | 2010 | 60 (61) | 30 (36) | 12 (17) |

T_{c,max} W2 | 2010 | 55 (42) | 20 (14) | 2 (0) |

T_{air,max} | 2010 | 53 (55) | 19 (20) | 10 (10) |

T_{c,max} W0 | 2011 | 55 (26) | 12 (8) | 1 (0) |

T_{c,max} W2 | 2011 | 44 (30) | 4(6) | 0 (0) |

T_{air,max} | 2011 | 42 (34) | 7 (6) | 0 (0) |

T_{c,max} W0 | 2013 | 55 (47) | 29 (33) | 3 (7) |

T_{c,max} W2 | 2013 | 37 (34) | 6 (5) | 0 (0) |

T_{air,max} | 2013 | 31 (29) | 9 (9) | 1 (1) |

T_{c,max} W0 | 2014 | 77 (13) | 36 (5) | 12 (0) |

T_{c,max} W2 | 2014 | 59 (68) | 15 (26) | 1 (3) |

T_{air,max} | 2014 | 54 (53) | 13 (18) | 0 (3) |

**Figure 8.**Extreme thermal unit sums above the optimal temperature of 20 °C (ETU, [52]) for the modeled mean daily crop temperature of the drought treatment plot (T

_{c,mean}W0, dotted line), the fully irrigated plot (T

_{c,mean}W2, dashed line) and for the measured mean daily air temperature (T

_{air,mean}, solid line) in 2010 (

**top left**), 2011 (

**top right**), 2013 (

**bottom left**), 2014 (

**bottom right**) (Hohenschulen research site).

## 4. Conclusions

## Acknowledgments

## Author Contributions

## Conflicts of Interest

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**MDPI and ACS Style**

Neukam, D.; Ahrends, H.; Luig, A.; Manderscheid, R.; Kage, H.
Integrating Wheat Canopy Temperatures in Crop System Models. *Agronomy* **2016**, *6*, 7.
https://doi.org/10.3390/agronomy6010007

**AMA Style**

Neukam D, Ahrends H, Luig A, Manderscheid R, Kage H.
Integrating Wheat Canopy Temperatures in Crop System Models. *Agronomy*. 2016; 6(1):7.
https://doi.org/10.3390/agronomy6010007

**Chicago/Turabian Style**

Neukam, Dorothee, Hella Ahrends, Adam Luig, Remy Manderscheid, and Henning Kage.
2016. "Integrating Wheat Canopy Temperatures in Crop System Models" *Agronomy* 6, no. 1: 7.
https://doi.org/10.3390/agronomy6010007