Water Stress Coefficient Ks Research Articles

Relationships between Leaf Area Index and Evapotranspiration and Crop Coefficient of Hilly Apple Orchard in the Loess Plateau

Drought and water shortage are the key factors that restrict the sustainable development of the apple industry in the Chinese Loess Plateau. The accurate prediction of ET can provide a scientific basis for water management of apple orchards. A study on the relationship between LAI, ET and crop coefficient Kc under water deficit is particularly necessary for the accurate prediction of ET in apple orchards. In this work, the crop coefficient Kc under water deficit was defined as the product of the crop coefficient KcI under no water stress and the water stress coefficient Ks, namely Kc = KcI × Ks. LAI and ET of the hilly apple orchard were measured from April to September in 2019 and 2020. The results showed: (1) The LAI of the apple orchard showed a trend of rapid increase—moderate increase—declined during the growth period, with 0.26–2.16 [m2 m−2] variation range; (2) The ET of the orchard was greater than the rainfall, the maximum ET was in July or August. The maximum components of ET in the apple orchard was E, with 47.8–49.1% of ET; T accounted for 42.5–43.9% of ET; Ic accounted for only 9.1–9.6% of ET; (3) There was a significant exponential relationship between the LAI and T or ET. The crop coefficient KcI under no water stress changed with the development of the apple tree canopy. The variation of water stress Ks was basically consistent with the variation of rainfall; (4) There is a significant exponential relationship between LAI and crop coefficient Kc under water deficit (Kc = 0.1141e1.0665LAI, R2 = 0.7055, p < 0.01). This study demonstrates that LAI could be used to estimate the crop coefficient Kc of apple orchards under water deficit in the Loess Plateau, and the actual evapotranspiration of apple orchards in this region could be predicted.

Improvement in estimation of soil water deficit by integrating airborne imagery data into a soil water balance model

Abstract: In this study, an approach that integrates airborne imagery data as inputs was used to improve the estimation of soil water deficit (SWD) for maize and sunflower grown under full and deficit irrigation treatments. The proposed model was applied to optimize the maximum total available soil water (TAWr) by minimizing the difference between a water stress coefficient ks and crop water stress index (1-CWSI). The optimal value of maximum TAWr was then used to calibrate a soil water balance model which in turn updated the estimation of soil water deficit. The estimates of SWD in the soil profile of both irrigated maize and sunflower fields were evaluated with the crop root zone SWD derived from neutron probe measurements and the FAO-56 SWD procedure. The results indicated a good agreement between the estimated SWD from the proposed approach and measured SWD for both maize and sunflower. The statistical analyses indicated that the maximum TAWr estimated from CWSI significantly improved the estimates of SWD, which reduced the mean absolute error (MAE) and root mean square error (RMSE) by 40% and 44% for maize and 22% for sunflower, compared with the FAO-56 model. The proposed procedure works better for crops under deficit irrigation condition. With the availability of higher spatial and temporal resolution airborne imagery during the growing season, the optimization procedure can be further improved. Keywords: soil water deficit, soil water balance model, airborne imagery, total available water, CWSI, deficit irrigation DOI: 10.3965/j.ijabe.20171003.3081 Citation: Zhang H H, Han M, Chavez J L, Lan Y B. Improvement in estimation of soil water deficit by integrating airborne imagery data into a soil water balance model. Int J Agric & Biol Eng, 2017; 10(3): 37–46.

Estimation of evapotranspiration and its components from an apple orchard in northwest China using sap flow and water balance methods

AbstractField experiments were conducted to investigate the effects of leaf area index and soil moisture content on evapotranspiration and its components within an apple orchard in northwest China for 2 years. Evapotranspiration in the non‐rainfall period was estimated using two approaches: the soil water balance method based on tube‐type time‐domain reflection measurements, and sap flow plus micro‐lysimeter methods. The two methods were in good agreement, with differences usually less than 10%. The components of evapotranspiration varied with canopy development. During spring and autumn, soil evaporation was dominating as result of low leaf area index. In summer, plant transpiration became significant, with an average transpiration to evapotranspiration ratio of 0·87. The crop coefficient Kc showed a strong linear dependence on leaf area index. The water stress coefficient Ks was around 1·0 when soil moisture was above 23% and started to decrease linearly after that. This study demonstrates that prediction of evapotranspiration in apple orchards can be made using the Food and Agriculture Organization's crop coefficient method from commonly available meteorological data in the area. Copyright © 2006 John Wiley & Sons, Ltd.

Estimating Soil Moisture Under Low Frequency Surface Irrigation Using Crop Water Stress Index

The present study investigated the relationship between the crop water stress index ~CWSI! and soil moisture for surface irrigated cotton ~Gossypium hirsutum, Delta Pine 90b! at Maricopa, Arizona during the 1998 season. The CWSI was linked to soil moisture through the water stress coefficient Ks that accounts for reduced crop evapotranspiration when there is a shortage of soil water. A stress recovery coefficient Krec was introduced to account for reduced crop evapotranspiration as the crop recovered from water stress after irrigation events. A soil water stress index ~SWSI! was derived in terms of Ks and Krec . The SWSI compared reasonably well to the CWSI, but atmospheric stability correction for the CWSI did not improve comparisons. When the CWSI was substituted into the SWSI formulation, it gave good prediction of soil moisture depletion ~fDEP; when to irrigate! and depth of root zone depletion ( Dr ; how much to irrigate!. Disagreement was greatest for fDEP,0.6 because cotton is less sensitive to water stress in this range.