Water is shortage and precious in Northwest China. That is way, during 24th May-1st June, the SHUI socio-economic team decided to dig in a water-saving investigation in Xinjiang Uygur Autonomous Region, China.
Agricultural water consumption accounts for about 87 % of the total water supply in Xinjiang. Cotton is the main planted crop there. The irrigation (e.g water) fee accounts for about 6% of the total cotton production cost. Most farmers here adopt water-saving technologies, mainly using drip irrigation under plastic film. However, for new water-saving irrigation technologies, most farmers are risk averse for the uncertainty along with new technologies. They are likely to depend on the government support.
Xinjiang is trying to promote Smart Agriculture, aiming to improve the water utilization efficient and irrigate more land with less water. Fot that reason, SHui WP5 team is undertaking extensive cost-benefit analyses of new water-saving technologies to increase the adopting ratio of new water-saving technologies to help farmers make the right decisions.
Exploring the value of long-term data sets to develop and evaluate agricultural management practices
Forty participants, with over 40% comprising ECRs, attended this event. Although participants were predominantly from EU countries, the wide appeal of the subject matter resulted in participants from outside the SHui consortium attending, including from Hungary, India and Pakistan.
Long-term data sets are essential for scientists to identify and understand agriculture’s ecological and environmental consequences to inform land managers and policy makers. They provide data that may also be used to develop theoretical models and to calibrate and validate simulation models. Long-term data can act as platforms for collaborative studies, thus promoting multidisciplinary research.
The webinar probed the following questions:
What are the benefits of long-term measurements at plot and catchment scales?
Do we measure the relevant parameters for hydrological modeling at catchment scale?
How can I benefit from SHui’s long-term data curation?
How can I use SHui data for decision making and agro-environmental modeling?
“Best management practices for optimized use of soil and water in agriculture” is now available and free to download.
This document provides a comprehensive review of Best Management Practices for optimized used soil and water in agricultural systems within the context of the SHui project.
The book will be soon available in Spanish and Chinese.
Rhizosheath, an unfamiliar word with a simple definition. Rhizosheath is simply the soil that gets stuck to the roots of a plant. Below- ground, this rhizosheath is the medium through which the plant interacts with its environment. Think of it like a glove, a very muddy root shaped glove. Bad analogies notwithstanding, scientists have suspected for some time that it’s pretty important and probably has a big impact on how plants grow, but we are not sure why exactly. There is some evidence that a larger rhizosheath may enhance plant drought resistance, by increasing the contact area of the roots with the surrounding soil, making it easier to drink up the limited water in the ground. So, a muddy root shaped glove with straws attached …. This analogy is truly getting out of hand.
Often it’s incredibly difficult to study the interaction between plant roots and soil, as it’s pretty difficult to see what’s happening underground. Despite this, it certainly seems worth the effort. With water scarcity set to become a near global problem in the next decade or two, we urgently need to find new ways of growing crops using less water. Understanding and exploiting these plant soil interactions could be key to doing just that. I’ve recently joined the SHui project, but have been working as a postdoctoral researcher at Lancaster University for almost 2 years. My current experiments (pictured) are investigating whether rhizosheath development of maize plants affects their physiological and biochemical responses to drought stress.
Our colleagues at Fujian Agriculture & Forestry University have recently identified the importance of plant hormone responses in regulating rhizosheath development https://onlinelibrary.wiley.com/doi/10.1111/pce.14036
a Independent Researcher, Variability, Ashalim 85512, Israel
b Department of Applied Mathematics, Delft University of Technology, Mourik Broekmanweg 6, 2628 XE Delft, the Netherlands
c TerraVision Lab, Midreshet Ben-Gurion 8499000, Israel
d Institute of Soil, Water and Environmental Sciences, Agricultural Research Organization, Gilat Research Center, Mobile post Negev 2, 85280, Israel
e Institute of Agricultural Engineering, Agricultural Research Organization, Volcani Center, P.O. Box 15159, Rishon LeZion 7505101, Israel
f The Robert H. Smith Institute of Plant Sciences and Genetics in Agriculture, The Hebrew University of Jerusalem, The Robert H. Smith Faculty of Agriculture, Food & Environment, Rehovot 76100, Israel
g Department of Soil and Water Sciences, The Robert H. Smith Faculty of Agriculture, Food and Environment, The Hebrew University of Jerusalem, P.O. Box 12, Rehovot 7610001, Israel
h Department of Precision Agriculture, MIGAL Galilee Research Institute, Kiryat Shmona 11016, Israel
i Department of Agriculture and Oenology, Eastern R&D Center, Israel
j Department of Chemical Engineering, Ariel University, Ariel 40700, Israel
Collection of accurate and representative data from agricultural fields is required for efficient crop management. Since growers have limited available resources, there is a need for advanced methods to select representative points within a field in order to best satisfy sampling or sensing objectives. The main purpose of this work was to develop a data-driven method for selecting locations across an agricultural field given observations of some covariates at every point in the field. These chosen locations should be representative of the distribution of the covariates in the entire population and represent the spatial variability in the field. They can then be used to sample an unknown target feature whose sampling is expensive and cannot be realistically done at the population scale.
An algorithm for determining these optimal sampling locations, namely the multifunctional matching (MFM) criterion, was based on matching of moments (functionals) between sample and population. The selected functionals in this study were standard deviation, mean, and Kendall’s tau. An additional algorithm defined the minimal number of observations that could represent the population according to a desired level of accuracy. The MFM was applied to datasets from two agricultural plots: a vineyard and a peach orchard. The data from the plots included measured values of slope, topographic wetness index, normalized difference vegetation index, and apparent soil electrical conductivity. The MFM algorithm selected the number of sampling points according to a representation accuracy of 90% and determined the optimal location of these points. The algorithm was validated against values of vine or tree water status measured as crop water stress index (CWSI). Algorithm performance was then compared to two other sampling methods: the conditioned Latin hypercube sampling (cLHS) model and a uniform random sample with spatial constraints. Comparison among sampling methods was based on measures of similarity between the target variable population distribution and the distribution of the selected sample.
MFM represented CWSI distribution better than the cLHS and the uniform random sampling, and the selected locations showed smaller deviations from the mean and standard deviation of the entire population. The MFM functioned better in the vineyard, where spatial variability was larger than in the orchard. In both plots, the spatial pattern of the selected samples captured the spatial variability of CWSI. MFM can be adjusted and applied using other moments/functionals and may be adopted by other disciplines, particularly in cases where small sample sizes are desired. View Full-Text
Agricultural productivity has vastly increased over recent decades as agricultural technologies have improved. However, future challenges such as population growth, land degradation and climate change mean that agricultural systems will have to keep adapting. Assessing the impacts of these global issues requires larger-scale perspectives rather than measurements within individual farmer’s fields. Work Package 3 (WP3) of the SHui project therefore assesses crop development over a regional to continental domain at a relatively high resolution (~1km). Thanks to the increasing availability of high resolution spatial data and enhanced computer systems, new possibilities in agricultural research can be explored at various scales.
During my Earth Science (BSc) and Physical Geography (MSc) degrees, I learned about surface and subsurface hydrological processes, land degradation, and how to work with various software and models. I have also done fieldwork in various countries and worked with other researchers, farmers, environmental agencies and meteorologists. My PhD research within SHui allows me to combine all these experiences and apply them to a more technical field of Geosciences. It has been a challenging but rewarding trajectory, with a very steep learning curve.
My research consists of two main parts. Initially, I developed and evaluated a spatially-distributed version of the field-scale AquaCrop model version 6.1. This regional version wraps AquaCrop in a parallel processing system, to make it run efficiently for any given resolution and domain. Various satellite products were used to evaluate biomass and surface soil moisture, with the findings recently submitted to the peer-reviewed journalGeoscientific Model Development.
Currently, I am applying satellite-based data assimilation to the AquaCrop model to improve the model simulations. For this, I use the Water Cloud Model to translate AquaCrop soil moisture and vegetation output into backscatter values, which are measured by active microwave satellites such as Sentinel-1. I will present preliminary findings at the IGARSS conference in Brussels in July. I am excited to work on this new topic, with the support of a research team that has much experience in Data Assimilation.
My overall aim is to build a robust and reliable spatially distributed version of AquaCrop, which can be applied to any region and for any crop type. I hope this spatial version of the model will provide more insight into regional changes of biomass production and soil moisture trends over time.
I’ve recently launched a new research webpage (https://ees.kuleuven.be/project/shui-regionalaquacrop) and gave a 2-minute virtual PICO presentation at EGU 2021 conference (see poster in Fig.1.). I consider myself lucky that COVID hasn’t affected my research, but I do look forward to the first conference that will be live again.
Figure 1. vPICO slide presented at EGU 2021 on 30-04-2021
Exploring hydrological processes in the Czech agricultural landscape
The Czech agricultural landscape is not only fascinating for photographers, but also interesting for hydrologists to observe the hydrological processes such as runoff generation, soil erosion in the cultivated fields. Thus, an experimental catchment has been established at the Czech village Nučice monitor the hydrological processes since 2011. When I saw the view in Nučice for the first time, I was amazed by the large parcel of crops, and the long wheel tracks within the beautiful landscape.
Figure 1. The view of the Czech agricultural landscape
During my work in the SHUI project, I had many opportunities to work in the field, which has gradually drawn my interests in the runoff generation mechanism and the runoff connectivity on the cultivated soils. As a PhD student at Czech Technical University in Prague, my research topic is related to the spatio-temporal variability of soil moisture in agricultural catchments. To identify spatio-temporal patterns in the catchment, we have implemented shallow soil moisture measurements at point-scale, hillslope-scale, and field-scale. We have deployed FDR (frequency domain reflectometry) sensors at different depths for point-scale measurements. The monitoring of topsoil water content at hillslope-scale and field-scale has been mostly accomplished by field surveys with HydroSense II sensors. Although the landscape is homogenous under the regular farming activities, the spatial variability of soil water content has been observed during the field surveys. Besides, we also deployed two COSMIC-ray sensors at the catchment to observe the dynamic of soil water content at field-scale.
Figure 2. Soil moisture measurements (HydroSense II, CS650 and cosmic-ray sensor)
To make our research be accessible for public, an open source web-based WALNUD dataset (Water in Agricultural Landscape – NUčice Database) has been established online for public use. The datasets consist of observed variables such as measured precipitation, air temperature, stream discharge, and soil moisture. The cross-seasonal, open access datasets at this small-scale agricultural catchment will benefit not only hydrologists but also local farmers. To promote the open dataset, we published a datanote on the journal Hydrological Processes. Also, I attended the CUAHSI seminar to give a brief introduction of our experimental catchment.
Figure 3. Field surveys of monitoring soil moisture
I grew up in the south part of China, the agricultural landscape in the southern China is quite different from where I am working now. The SHUI project has bridged the researches from multiple disciplines across EU and China, which has broadened my horizons about the different agricultural practices and water managements. Facing the challenges of climate change and water scarcity, I would like to gain more knowledge of water and soil management to contribute to our common future.
—
Further reading:
Li, T.; Jeřábek, J; Noreika, N.; Dostál, T.; Zumr, D. (2021): An overview of hydrometeorological datasets from a small agricultural catchment (Nučice) in the Czech Republic. In: Hydrological Processes. DOI: 10.1002/hyp.14042.
Noreika, N.; Li, T.; Zumr, D.; Krása, J.; Dostál, T.; Srinivasan, R. (2020): Farm-scale biofuel crop adoption and its effects on in-basin water balance. SUSTAINABILITY. 2020, 12(24), ISSN 2071-1050. DOI 10.3390/su122410596.
Jeřábek, J., Zumr, D., & Dostál, T. (2017). Identifying the plough pan position on cultivated soils by measurements of electrical resistivity and penetration resistance. Soil and Tillage Research, 174, 231–240. https://doi.org/10.1016/j.still.2017.07.008
Zumr, David; Dostál, Tomáš; Devátý, Jan (2015): Identification of prevailing storm runoff generation mechanisms in an intensively cultivated catchment. In: Journal of Hydrology and Hydromechanics 63 (3), S. 246–254. DOI: 10.1515/johh-2015-0022.
Zumr, David; Dostál, Tomáš; Devátý, Jan; Valenta, Petr; Rosendorf, Pavel; Eder, Alexander; Strauss, Peter (2017): Experimental determination of the flood wave transformation and the sediment resuspension in a small regulated stream in an agricultural catchment. In: Hydrol. Earth Syst. Sci. 21 (11), S. 5681–5691. DOI: 10.5194/hess-21-5681-2017.
Water – it is life’s most essential resource. We use it as recreation, to travel, to grow crops, to quench our own thirst. As such, water conservation is a multifaceted societal issue with expanding and diverse career opportunities. A career in water conservation can wear many different masks and is highly multidisciplinary.
I began my career in water conservation while studying for my master’s in Aquatic Resources at Texas State University (TSU) in Texas, USA. My research at TSU involved population estimates and community structure evaluations of endangered aquatic invertebrates in crenic habitats. In this role I travelled to western Texas frequently to collect samples that I later processed under a dissecting microscope. In total, I counted over 150,000 snails and amphipods. These species are endangered largely due to the over-pumping of groundwater for agricultural uses and oil exploration.
Studying these tiny invertebrates prompted me to consider the larger issues at play: responsible, sustainable water and landscape management. I knew that I wanted the next step of my education to focus on water conservation, which lead me to Czech Technical University (CTU) in Prague.
Since beginning my PhD studies at CTU, I have been involved in many departmental projects and have also had the opportunity to develop my own research questions and thesis topic. I have participated in field experiments that study the effects of varied crop and soil treatments on runoff processes using an outdoor rainfall simulator as well as catchment-scale topsoil water content surveys.
Figure 1. Phantom Lake Spring near Balmorhea, Texas, USAFigure 2. Tryonia cheatumi, an endangered aquatic snail species found in springs of western Texas, USA.Figure 3. Rainfall simulation experimental setup on an agricultural plot near Řisuty, Czech Republic.
My thesis topic is titled “modeling hydrological impacts of management practices in rural catchments using SWAT.” SWAT, or the Soil and Water Assessment Tool, is a semi-physically based, semi-distributed, basin-scale hydrologic model. It’s primarily used to model agricultural catchments and has been applied all around the world. A goal of my PhD work is to apply the SWAT model to two catchments in the Czech Republic.
Figure 4. Conducting a topsoil water content survey at the Nučice experimental basin near Prusice, Czech Republic. Photo credit: Tailin Li.Figure 5. A map of the Czech Republic with Prague highlighted, as well as my two study basins, Nučice and Vrchlice.
I am most interested in the application of agricultural conservation practices in the Czech landscape and how the adoption of such practices will affect the small water cycle. The Czech landscape is still recovering from agricultural intensification that occurred during the Communist era, which included increasing field sizes, widespread subsurface tile drainage systems (so that soils drain faster than they would naturally), and concrete-lined and straightened streams. In the small water cycle: water should infiltrate the soil where it falls as rain, surface runoff should be minimized, natural drainage patterns should be restored, and the water holding capacity of soils should be increased. Not only do agricultural conservation practices help to reinforce the small water cycle, but they also aim to build healthier agriculture soils and to reduce sediment and nutrient runoff into our freshwater systems. For example, contour farming reduces surface runoff by impounding water in small depressions and reduces soil loss by decreasing the erosive power of the surface runoff while crop residues increase infiltration and reduce surface runoff by decreasing surface sealing.
Figure 6. Representation of the small water cycle (Kravčik 2015)Figure 7. Agricultural field near Řisuty, Czech Republic.Figure 8. Field size discrepancies at the border of Austria and the Czech Republic. (Esri 2021)
In the field of water conservation, there are many professional trajectories that can be followed across the biological, physical, and political sciences. I am unsure exactly what my future career may look like, but so far, my career has included: sitting at a microscope for countless hours, SCUBA diving to collect water and invertebrate samples, conducting field experiments in the Czech countryside, and developing a hydrologic model that should (in theory) simulate the real thing.
Whatever the future may hold, one thing remains constant – the way we treat the water and land around us matters. If my research can help the conservation efforts of one little snail species or help one farmer make informed management decisions, I know I will have done my part.
