Winter wheat and maize under varying soil moisture: from leaf to canopy

2021 ◽  
Author(s):  
Thuy Huu Nguyen ◽  
Matthias Langensiepen ◽  
Thomas Gaiser ◽  
Heidi Webber ◽  
Hella Ahrends ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Water Stress ◽  
Winter Wheat ◽  
Leaf Area ◽  
Dynamic Change ◽  
Crop Growth ◽  
Leaf Water ◽  
Area Index ◽  
Canopy Transpiration ◽  
Stomatal Responses

<p>Drought is one of the most detrimental factors limiting crop growth and production of important staple crops such as winter wheat and maize. For both crops, stomatal regulation and change of canopy structure responses to water stress can be observed. A substantial range of stomatal behavior in regulating water loss was recently reported while the crop growth and morphological responses to drought stress depend on the intensity and duration of the imposed stress. Insights into the responses from leaf to the canopy are important for crop modeling and soil-vegetation-atmosphere models (SVAT). Stomatal responses and effects of soil water deficit on the dynamic change of canopy photosynthesis and transpiration, and seasonal crop growth of winter wheat and maize are investigated based on data collected from field-grown conditions with varying soil moisture treatments (sheltered, rainfed, irrigated) in 2016, 2017, and 2018. A reduction of leaf net photosynthesis (An), stomatal conductance (Gs), transpiration (E), and leaf water potential (LWP) was observed in the sheltered plot as compared to the rainfed and irrigated plots in winter wheat in 2016, indicating anisohydric stomatal responses. Maize showed seasonal isohydric behaviour with the minimum LWP from -1.5 to -2 MPa in 2017 and -2 to -2.7 MPa in the extremely hot and dry year in 2018. Crop growth (biomass, leaf area index, and yield) was substantially reduced under drought conditions, particularly for maize in 2018. Leaf water use efficiency (An/E) and crop WUE (total dry biomass/canopy transpiration) were not significantly different among treatments in both crops. The reduction of tiller number (in winter wheat) and leaf-rolling and plant size (in maize) resulted in a reduction of canopy transpiration, assimilation rate, and thus biomass. The seasonal isohydry in maize and the seasonal variability of LWP in winter wheat suggest a possibility to use the same critical LWP thresholds for maize and wheat to simulate the stomatal control in process-based crop and SVAT models. The canopy response such as dynamically reducing leaf area under water stress adds complexity in simulating gas exchange and crop growth rate that needs adequate consideration in the current modeling approaches.</p>

scholarly journals Grazing winter wheat relieves plant water stress and transiently enhances photosynthesis

2010 ◽  
Vol 37 (8) ◽  
pp. 726 ◽  
Author(s):  
Matthew T. Harrison ◽  
Walter M. Kelman ◽  
Andrew D. Moore ◽  
John R. Evans
Keyword(s):  
Water Stress ◽  
Leaf Area ◽  
Water Status ◽  
Leaf Water ◽  
Plant Water ◽  
Rubisco Activity ◽  
Leaf Water Status ◽  
Area Index ◽  
Plant Water Stress ◽  
The Impact

To model the impact of grazing on the growth of wheat (Triticum aestivum L.), we measured photosynthesis in the field. Grazing may affect photosynthesis as a consequence of changes to leaf water status, nitrogen content per unit leaf area (Na) or photosynthetic enzyme activity. While light-saturated CO2 assimilation rates (Asat) of field-grown wheat were unchanged during grazing, Asat transiently increased by 33–68% compared with ungrazed leaves over a 2- to 4-week period after grazing ended. Grazing reduced leaf mass per unit area, increased stomatal conductance and increased intercellular CO2 concentrations (Ci) by 36–38%, 88–169% and 17–20%, respectively. Grazing did not alter Na. Using a photosynthesis model, we demonstrated that the increase in Asat after grazing required an increase in Rubisco activity of up to 53%, whereas the increase in Ci could only increase Asat by up to 13%. Increased Rubisco activity was associated with a partial alleviation of leaf water stress. We observed a 68% increase in leaf water potential of grazed plants that could be attributed to reduced leaf area index and canopy evaporative demand, as well as to increased rainfall infiltration into soil. The grazing of rain-fed grain cereals may be tailored to relieve plant water stress and enhance leaf photosynthesis.

scholarly journals A Coupled Model for Simulating Water and Heat Transfer in Soil-Plant-Atmosphere Continuum with Crop Growth

Water ◽  
2018 ◽  
Vol 11 (1) ◽  
pp. 47 ◽  
Author(s):  
Weicai Yang ◽  
Xiaomin Mao ◽  
Jian Yang ◽  
Mengmeng Ji ◽  
Adebayo J. Adeloye
Keyword(s):  
Heat Transfer ◽  
Soil Moisture ◽  
Winter Wheat ◽  
Soil Water ◽  
Coupled Model ◽  
Crop Growth ◽  
Soil Water Storage ◽  
Area Index ◽  
Moisture Condition ◽  
Soil Moisture Condition

Crop growth is influenced by the energy partition and water–heat transfer in the soil and canopy, while crop growth affects the land surface energy distribution and soil water-heat dynamics. In order to simulate the above processes and their interactions, a new model, named CropSPAC, was developed considering both the growth of winter wheat and the water–heat transfer in Soil-Plant-Atmosphere Continuum (SPAC). In CropSPAC, the crop module depicts the dynamic changes of leaf area index (LAI), crop height, and the root distribution and outputs them to the SPAC module, while the latter outputs soil moisture conditions for the crop module. CropSPAC was calibrated and validated by field experiment of winter wheat in Yongledian, Beijing, with five levels of irrigation treatments, namely W0 (0 mm), W1 (60 mm), W2 (110 mm), W3 (170 mm), and W4 (230 mm). Results show that CropSPAC could predict the soil water and temperature distribution, and winter wheat growth with acceptable accuracy. For example, for the 0–1 m soil water storage, the R2 for W0, W1, W2, W3, and W4 is 0.90, 0.88, 0.90, 0.91, and 0.79, and the root mean square error (RMSE) is 17.24 mm, 27.65 mm, 20.47 mm, 22.35 mm, and 12.88 mm, respectively. For soil temperature along the soil profile, the R2 ranges between 0.96 and 0.98, and the RMSE between 1.22 °C and 1.94 °C. For LAI, the R2 varied from 0.76 to 0.96, and the RMSE from 0.52 to 0.67. We further compared the simulation results by CropSPAC and its two detached modules, i.e., crop and the SPAC modules. Results demonstrate that the coupled model could better reflect the interactions between crop growth and soil moisture condition, more suitable to be used under deficit irrigation conditions.

scholarly journals Optimization of a coupled hydrology-crop growth model through the assimilation of observed soil moisture and leaf area index values using an ensemble Kalman filter

2007 ◽  
Vol 43 (4) ◽  
Author(s):  
Valentijn R. N. Pauwels ◽  
Niko E. C. Verhoest ◽  
Gabriëlle J. M. De Lannoy ◽  
Vincent Guissard ◽  
Cozmin Lucau ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Kalman Filter ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Growth Model ◽  
Ensemble Kalman Filter ◽  
Crop Growth ◽  
Crop Growth Model ◽  
Area Index

scholarly journals Prediction of Winter Wheat Maturity Dates through Assimilating Remotely Sensed Leaf Area Index into Crop Growth Model

2020 ◽  
Vol 12 (18) ◽  
pp. 2896
Author(s):  
Wen Zhuo ◽  
Jianxi Huang ◽  
Xinran Gao ◽  
Hongyuan Ma ◽  
Hai Huang ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Winter Wheat ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Growth Model ◽  
Crop Growth ◽  
Crop Growth Model ◽  
Area Index ◽  
Maturity Date ◽  
Crop Maturity

Predicting crop maturity dates is important for improving crop harvest planning and grain quality. The prediction of crop maturity dates by assimilating remote sensing information into crop growth model has not been fully explored. In this study, a data assimilation framework incorporating the leaf area index (LAI) product from Moderate Resolution Imaging Spectroradiometer (MODIS) into a World Food Studies (WOFOST) model was proposed to predict the maturity dates of winter wheat in Henan province, China. Minimization of normalized cost function was used to obtain the input parameters of the WOFOST model. The WOFOST model was run with the re-initialized parameter to forecast the maturity dates of winter wheat grid by grid, and THORPEX Interactive Grand Global Ensemble (TIGGE) was used as forecasting period weather input in the future 15 days (d) for the WOFOST model. The results demonstrated a promising regional maturity date prediction with determination coefficient (R2) of 0.94 and the root mean square error (RMSE) of 1.86 d. The outcomes also showed that the optimal forecasting starting time for Henan was 30 April, corresponding to a stage from anthesis to grain filling. Our study indicated great potential of using data assimilation approaches in winter wheat maturity date prediction.

Response of four grain legumes to water stress in south-eastern Queensland. IV. Interaction with sowing arrangement

1983 ◽  
Vol 34 (6) ◽  
pp. 661 ◽  
Author(s):  
RJ Lawn
Keyword(s):  
Water Stress ◽  
Population Density ◽  
Water Use ◽  
Spatial Arrangement ◽  
Crop Growth ◽  
Yield Response ◽  
Crop Water ◽  
Area Index ◽  
Crop Water Use ◽  
South Eastern

The effect of spatial arrangement and population density on growth, dry matter production, yield and water use of black gram (Vigna mungo cv. Regur), green gram (V. radiata cv. Berken), cowpea (V. unguiculata CPI 28215) and soybean (Glycine rnax CP126671), under irrigated, rain-fed fallowed and rain-fed double-cropped culture was evaluated at Dalby in south-eastern Queensland. Equidistant spacings increased initial rates of leaf area index (LAI) development and crop water use compared with 1-m rows at the same population densities. In the irrigated and rain-fed fallowed treatments, where more water was available for crop growth, both seed yields and total crop water use were higher in the equidistant spacings. However, in the double-cropped treatment, where water availability was limited, there was no yield difference between rows and equidistant spacings, primarily because initially faster growth in the latter was offset by more severe water stress later in the season. Higher population density also increased initial crop growth rate and water use, particularly in the equidistant spacings. However, there was no significant yield response to density, presumably because subsequent competition for light/ water offset initial effects on growth. Although absolute yield differences existed between legume cultivars within cultural treatments, there were no significant differential responses to either spatial arrangement or population density among these four cultivars.

scholarly journals Representing winter wheat in the Community Land Model (version 4.5)

2017 ◽  
Vol 10 (5) ◽  
pp. 1873-1888 ◽  
Author(s):  
Yaqiong Lu ◽  
Ian N. Williams ◽  
Justin E. Bagley ◽  
Margaret S. Torn ◽  
Lara M. Kueppers
Keyword(s):  
Heat Flux ◽  
Winter Wheat ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Latent Heat Flux ◽  
Carbon Allocation ◽  
Net Ecosystem Exchange ◽  
Area Index ◽  
Community Land Model ◽  
The Us

Abstract. Winter wheat is a staple crop for global food security, and is the dominant vegetation cover for a significant fraction of Earth's croplands. As such, it plays an important role in carbon cycling and land–atmosphere interactions in these key regions. Accurate simulation of winter wheat growth is not only crucial for future yield prediction under a changing climate, but also for accurately predicting the energy and water cycles for winter wheat dominated regions. We modified the winter wheat model in the Community Land Model (CLM) to better simulate winter wheat leaf area index, latent heat flux, net ecosystem exchange of CO2, and grain yield. These included schemes to represent vernalization as well as frost tolerance and damage. We calibrated three key parameters (minimum planting temperature, maximum crop growth days, and initial value of leaf carbon allocation coefficient) and modified the grain carbon allocation algorithm for simulations at the US Southern Great Plains ARM site (US-ARM), and validated the model performance at eight additional sites across North America. We found that the new winter wheat model improved the prediction of monthly variation in leaf area index, reduced latent heat flux, and net ecosystem exchange root mean square error (RMSE) by 41 and 35 % during the spring growing season. The model accurately simulated the interannual variation in yield at the US-ARM site, but underestimated yield at sites and in regions (northwestern and southeastern US) with historically greater yields by 35 %.

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