scholarly journals Optimization of canopy conductance models from concurrent measurements of sap flow and stem water potential on Drooping Sheoak in South Australia

2014 ◽  
Vol 50 (7) ◽  
pp. 6154-6167 ◽  
Author(s):  
Hailong Wang ◽  
Huade Guan ◽  
Zijuan Deng ◽  
Craig T. Simmons
2006 ◽  
Vol 279 (1-2) ◽  
pp. 229-242 ◽  
Author(s):  
M. F. Ortuño ◽  
Y. García-Orellana ◽  
W. Conejero ◽  
M. C. Ruiz-Sánchez ◽  
O. Mounzer ◽  
...  

2012 ◽  
pp. n/a-n/a ◽  
Author(s):  
Yuting Yang ◽  
Huade Guan ◽  
John L. Hutson ◽  
Hailong Wang ◽  
Caecilia Ewenz ◽  
...  

2021 ◽  
Vol 13 (4) ◽  
pp. 1759
Author(s):  
Said A. Hamido ◽  
Kelly T. Morgan

The availability and proper irrigation scheduling of water are some of the most significant limitations on citrus production in Florida. The proper volume of citrus water demand is vital in evaluating sustainable irrigation approaches. The current study aims to determine the amount of irrigation required to grow citrus trees at higher planting densities without detrimental impacts on trees’ water relation parameters. The study was conducted between November 2017 and September 2020 on young sweet orange (Citrus sinensis) trees budded on the ‘US-897’ (Cleopatra mandarin x Flying Dragon trifoliate orange) citrus rootstock transplanted in sandy soil at the Southwest Florida Research and Education Center (SWFREC) demonstration grove, near Immokalee, Florida. The experiment contained six planting densities, including 447, 598, and 745 trees per ha replicated four times, and 512, 717, and 897 trees per ha replicated six times. Each density treatment was irrigated at 62% or 100% during the first 15 months between 2017 and 2019 or one of the four irrigation rates (26.5, 40.5, 53, or 81%) based on the calculated crop water supplied (ETc) during the last 17 months of 2019–2020. Tree water relations, including soil moisture, stem water potential, and water supplied, were collected periodically. In addition, soil salinity was determined. During the first year (2018), a higher irrigation rate (100% ETc) represented higher soil water contents; however, the soil water content for the lower irrigation rate (62% ETc) did not represent biological stress. One emitter per tree regardless of planting density supported stem water potential (Ψstem) values between −0.80 and −0.79 MPa for lower and full irrigation rates, respectively. However, when treatments were adjusted from April 2019 through September 2020, the results substantially changed. The higher irrigation rate (81% ETc) represented higher soil water contents during the remainder of the study, the lower irrigation rate (26.5% ETc) represents biological stress as a result of stem water potential (Ψstem) values between −1.05 and −0.91 MPa for lower and higher irrigation rates, respectively. Besides this, increasing the irrigation rate from 26.5% to 81%ETc decreased the soil salinity by 33%. Although increasing the planting density from 717 to 897 trees per hectare reduced the water supplied on average by 37% when one irrigation emitter was used to irrigate two trees instead of one, applying an 81% ETc irrigation rate in citrus is more efficient and could be managed in commercial groves.


2021 ◽  
Vol 13 (9) ◽  
pp. 1837
Author(s):  
Eve Laroche-Pinel ◽  
Sylvie Duthoit ◽  
Mohanad Albughdadi ◽  
Anne D. Costard ◽  
Jacques Rousseau ◽  
...  

Wine growing needs to adapt to confront climate change. In fact, the lack of water becomes more and more important in many regions. Whereas vineyards have been located in dry areas for decades, so they need special resilient varieties and/or a sufficient water supply at key development stages in case of severe drought. With climate change and the decrease of water availability, some vineyard regions face difficulties because of unsuitable variety, wrong vine management or due to the limited water access. Decision support tools are therefore required to optimize water use or to adapt agronomic practices. This study aimed at monitoring vine water status at a large scale with Sentinel-2 images. The goal was to provide a solution that would give spatialized and temporal information throughout the season on the water status of the vines. For this purpose, thirty six plots were monitored in total over three years (2018, 2019 and 2020). Vine water status was measured with stem water potential in field measurements from pea size to ripening stage. Simultaneously Sentinel-2 images were downloaded and processed to extract band reflectance values and compute vegetation indices. In our study, we tested five supervised regression machine learning algorithms to find possible relationships between stem water potential and data acquired from Sentinel-2 images (bands reflectance values and vegetation indices). Regression model using Red, NIR, Red-Edge and SWIR bands gave promising result to predict stem water potential (R2=0.40, RMSE=0.26).


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