Managing water scarcity in European and Chinese cropping systems

In a refreshingly rare departure from the recent routine of online meetings, Lancaster Environment Centre hosted Tony Peloe of Delta-T Devices for a workshop covering the theory and practice of measuring soil and plant water status. To comply with University regulations on social distancing, workshop numbers were restricted to 12 (maximum capacity of the room) but nevertheless included 6 SHui-affiliated researchers: Jaime Puertolas (now a lecturer at La Laguna, Tenerife), Ryan Edge (currently Lancaster’s PDRA), Cristina McBride-Serrano (still in a technical role at Lancaster but moving to PhD studies shortly), Jing Chen (visiting from Yangzhou University, China), Yingying Ma (visiting from Northwest Agricultural & Forestry University, China) and Leonardo da Silva (visiting from University of Rio de Janeiro, Brazil). Each researcher was able to appraise the suitability of Delta-T’s extensive instrumentation range to help them address their research objectives.

Researcher familiarity with these instruments ranged from novice to highly experienced, so it was possible to pair researchers of different experience to swap best practice. Yahya Khan (visiting from University of Agriculture Faisalabad, Pakistan to research the impact of rhizobacteria on legume drought tolerance) won the “porometer calibration competition” with an error of just 3.3%, although further studies are needed to determine the influence of operator and specific porometer used, and any interaction ! In addition to briefing the group on porometer best practice, Tony introduced the extensive range of soil moisture instrumentation: PR2, SM150, WET sensor and the new digital WET150 soil sensor. After calibration exercises, sensor outputs were compared to understand the capacities of different sensors.

Selected attendee reflections were:

  • Although I am an experienced user of most of the equipment shown, there were some interesting new features and gadgets that I could learn of.
  • The workshop was already well managed and well organized. I don’t believe it needs further improvement but, it would be good if these types of workshops are arranged frequently to provide recent advancement in instrumentation and working principles.
  • Tony was incredibly knowledgeable, patient and helpful and I know my confidence has grown when using Delta-T equipment.
  • In China, such workshops would have had much greater attendance, so the smaller number of participants and relatively large numbers of the instruments allowed us hands-on personal experience and instruction.

Author: Lang Lili

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.

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?

Slides for download.

First Shui book release!

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.

Link: http://dx.doi.org/10.20350/digitalCSIC/13964

Author: Dr Ryan Edge

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

&

Previous work at Lancaster has suggested that enhanced rhizosheath development may limit soil erosion  https://onlinelibrary.wiley.com/doi/full/10.1111/ejss.13042

Root hairs enhance rhizosheath development in a range of species

(https://academic.oup.com/aob/advance-article/doi/10.1093/aob/mcab029/6149919 )

and Scottish colleagues recently showed the importance of in stabilising crop yield in dry years

https://academic.oup.com/aob/advance-article/doi/10.1093/aob/mcaa181/5920680

Certainly it’s an exciting time for me to be joining the SHui project to work on rhizosheath development.

N.Ohana-Levia1A.Derumignyb1A.PeeterscA.Ben-GaldI.BahatefL.KatzdefgY.NetzerijA.NaorhY.Cohene

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

Computers and Electronics in Agriculture https://doi.org/10.1016/j.compag.2021.106262

Abstract

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

Keywords: Partially-observed data, Representative sampling given covariates, Two-phase study, Agricultural sampling, Spatial autocorrelation

On the 19th July the 3rd session of the Sichuan-Israel New Type Agricultural Technology Training started.

On June 3rd Spanish team held its Focus Group after postponed it several times due to COVID lockdown. This meeting was moderated by Ana Sanchez Montero (SHui Project Manager) because of travel restriction for the Göttingen University due to the pandemic.

Despite the difficulties, the discussion on “Water management in Agriculture” was successful in a region where water is a scarce commodity. Farmers were focused on woody crops: olive trees, vines, almond and other nuts, stone fruit trees.

Link to the news story at the “ABC” newspaper: https://sevilla.abc.es/agronoma/noticias/cultivos/aceites-de-oliva/dop-estepa-mapa-erosion/