23 research outputs found
Evaluation of an operational real-time irrigation scheduling scheme for drip irrigated citrus fields in Picassent, Spain
[EN] Irrigated agriculture is very important for securing food production for an increasing population over the next decades. Given scarcity of water resources, optimal irrigation management is needed to reduce water while realizing maximal crop productivity. The new method of integrating soil water content measurements and the Community Land Model (CLM) using sequential data assimilation (DA) is promising to improve the prediction of soil water status and efficiently design irrigation strategies. Soil water content measured by FDR (Frequency Domain Reflectometry) was assimilated into CLM by LETKF (Local Ensemble Transform Kalman Filter) to improve model predictions. Atmospheric input data from GFS (Global Forecast System) were used to force CLM in order to predict short-term soil water contents. The irrigation amount was then calculated on the basis of the difference between predicted and targeted soil water content over the root zone.
During the real-time irrigation campaigns in Picassent (Spain) in 2015 and 2016, there were 6 fields irrigated according the data assimilation-optimization approach (CLM-DA), 2 further fields according the FAO (Food and Agriculture Organization) water balance method and also 2 fields traditionally according the farmers preference. The required amount of irrigation water for each citrus field was applied by SCADA (supervisory control and data acquisition system). Compared with the traditionally irrigated fields by farmers, 24% less irrigation water was needed for the CLM-DA scheduled fields averaged over both years from July to September, while the FAO fields were irrigated with 22% less water. Stem water potential data and soil moisture recordings of the CLM-DA scheduled fields did not indicate significant water stress during the irrigation period. The CLM-DA scheduled fields received less irrigation water than traditionally irrigated fields, but the orange production was not significantly suppressed.
Overall, our results show that the CLM-DA method is attractive given its water saving potential and automated approach, ease of incorporation of on-line measurements and ensemble based predictions of soil moisture evolution.The first author of this paper was funded by a stipend from the Government of China (CSC scholarship). The support of the super computing facilities of Forschungszentrum Juelich (JURECA) is gratefully acknowledged. We are also thankful to our colleagues in IVIA and Universitat Politecnica de Valencia for the installation of soil moisture sensors and conducting stem water potential measurements.Li, D.; Hendricks, H.; Han, X.; Jiménez Bello, MA.; Martínez Alzamora, F.; Vereeken, H. (2018). Evaluation of an operational real-time irrigation scheduling scheme for drip irrigated citrus fields in Picassent, Spain. Agricultural Water Management. 208:465-477. https://doi.org/10.1016/j.agwat.2018.06.022S46547720
Can we better understand severe mental illness through the lens of Syndemics?
Current health care systems do not sufficiently address contributors, also known as modifiable behavior factors, to severe mental illnesses (SMI). Instead treatment is focused on decreasing symptom-experience rather than reducing the detrimental effect of biological predisposition and behavioral influences on illness. Health care services and patients alike call for a more comprehensive, individual approach to mental health care, especially for people with SMI. A Syndemics framework has been previously used to identify ecological and social contributors to an HIV epidemic in the 1990s, and the same framework is transferable to mental health research to identify the relationship between contributing factors and the outcomes of SMI. Using this approach, a holistic insight into mental illness experience could inform more effective health care strategies that lessen the burden of disease on people with SMI. In this review, the components of a Syndemic framework, the scientific contributions to the topic so far, and the possible future of mental health research under the implementation of a Syndemic framework approach are examined