scholarly journals Estimate Cotton Water Consumption from Shallow Groundwater under Different Irrigation Schedules

Agronomy ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 213
Author(s):  
Guohua Zhang ◽  
Xinhu Li
Keyword(s):  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Water Consumption ◽  
Meteorological Factors ◽  
Shallow Groundwater ◽  
Soil Evaporation ◽  
Groundwater Depth ◽  
Crop Water ◽  
Crop Water Consumption

Shallow groundwater is considered an important water resource to meet crop irrigation demands. However, limited information is available on the application of models to investigate the impact of irrigation schedules on shallow groundwater depth and estimate evaporation while considering the interaction between meteorological factors and the surface soil water content (SWC). Based on the Richards equation, we develop a model to simultaneously estimate crop water consumption of shallow groundwater and determine the optimal irrigation schedule in association with a shallow groundwater depth. A new soil evaporation function was established, and the control factors were calculated by using only the days after sowing. In this study, two irrigation scheduling methods were considered. In Method A, irrigation was managed based on the soil water content; in Method B, irrigation was based on the crop water demand. In comparison with Method B, Method A was more rational because it could use more groundwater, and the ratio of soil evaporation to total evapotranspiration was low. In this model, the interaction between meteorological factors and the SWC was considered to better reflect the real condition; therefore, the model provided a better way to estimate the crop water consumption.

Deficit irrigation enhances contribution of shallow groundwater to crop water consumption in arid area

2017 ◽  
Vol 185 ◽  
pp. 116-125 ◽  
Author(s):  
Xiaoyu Gao ◽  
Yining Bai ◽  
Zailin Huo ◽  
Xu Xu ◽  
Guanhua Huang ◽  
...  
Keyword(s):  
Water Consumption ◽  
Deficit Irrigation ◽  
Shallow Groundwater ◽  
Arid Area ◽  
Crop Water ◽  
Crop Water Consumption

scholarly journals Processing Soil Water Content Measurements to Estimate Crop Water Requirements and Optimize Irrigation Supply

2019 ◽  
Vol 52 (23) ◽  
pp. 49-53
Author(s):  
F. Karam ◽  
A. Mouneimne ◽  
F. Aichouche ◽  
A. Rapaport ◽  
J. Harmand
Keyword(s):  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Crop Water ◽  
Water Requirements ◽  
Crop Water Requirements ◽  
Irrigation Supply

scholarly journals Predicting Soil Water Content on Rainfed Maize through Aerial Thermal Imaging

2021 ◽  
Vol 3 (4) ◽  
pp. 942-953
Author(s):  
Matheus Gabriel Acorsi ◽  
Leandro Maria Gimenez
Keyword(s):  
Linear Regression ◽  
Spatial Variability ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Water Status ◽  
Canopy Temperature ◽  
Crop Water ◽  
Soil Water Status ◽  
Physical Attributes

Restrictions on soil water supply can dramatically reduce crop yields by affecting the growth and development of plants. For this reason, screening tools that can detect crop water stress early have been long investigated, with canopy temperature (CT) being widely used for this purpose. In this study, we investigated the relationship between canopy temperature retrieved from unmanned aerial vehicles (UAV) based thermal imagery with soil and plant attributes, using a rainfed maize field as the area of study. The flight mission was conducted during the late vegetative stage and at solar noon, when a considerable soil water deficit was detected according to the soil water balance model used. While the images were being taken, soil sampling was conducted to determine the soil water content across the field. The sampling results demonstrated the spatial variability of soil water status, with soil volumetric water content (SVWC) presenting 10.4% of variation and values close to the permanent wilting point (PWP), reflecting CT readings that ranged from 32.8 to 40.6 °C among the sampling locations. Although CT correlated well with many of the physical attributes of soil that are related to water dynamics, the simple linear regression between CT and soil water content variables yielded coefficients of determination (R2) = 0.42, indicating that CT alone might not be sufficient to predict soil water status. Nonetheless, when CT was combined with some soil physical attributes in a multiple linear regression, the prediction capacity was significantly increased, achieving an R2 value = 0.88. This result indicates the potential use of CT along with certain soil physical variables to predict crop water status, making it a useful tool for studies exploring the spatial variability of in-season drought stress.

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