Managing water scarcity in European and Chinese cropping systems

My name is Leonardo Duarte Batista da Silva and I’m a Professor at the Federal Rural University of Rio de Janeiro (Universidade Federal Rural do Rio de Janeiro). In Brazil I conduct research on environmental engineering, water resources and irrigation.

I received funding from the Brazilian government through CNPq ( to sponsor my sabbatical year at the Lancaster Environment Centre (LEC) together with Professor Ian Dodd and his team from May 2021 until June 2022.

My research focuses on whether partial root zone drying (PRD) increases water use efficiency of tomato by increasing plant ABA concentrations, when plants are grown at different levels of soil water availability. Within the greenhouse, I’ve grown plants in 5 liter pots divided with a vertical partition and have already measured soil moisture and stomatal conductance to understand plant responses. I’ve been helped by attending Delta-T Devices training workshops, arranged within the SHui Project, to learn the instrumentation and available software. In future experiments I will measure leaf water potential and ABA levels to understand the regulation of stomatal responses. I’d like to thank Prof Ian Dodd and his entire team, as well as Lancaster University, for hosting my visit. Also special thanks to CNPq for funding me and the Federal Rural University of Rio de Janeiro for giving me the opportunity to develop this important work, as it will enhance my interactions with other Brazilian colleagues who are conducting field work on PRD on typical Brazilian crops such as coffee and papaya.

Author: Jakub Jeřábek, PhD student at Czech Technical University in Prague

“You study mud”. This is how people often respond when I tell them what I study. Don’t take me wrong. Mud is cool. But the topics I am interested in are transport processes of water (and other substances) into soil.

Artificial rainfall experiment. Study of tillage and wheel tracks effects on the runoff generation and soil loss.

Water and soil are both crucial natural resources. Agriculture is a human activity that alters enormous areas of the earth surface and changes soils and water dynamics.  Good soil can hold water which can be used by plants during a dry period and can lead the water through in the case of rainfall at the same time. It is therefore necessary to study soil and water and understand processes behind this.

My background began in landscape engineering where we studied all sorts of aspects of the landscape. However, after some time my attention gravitated to water and soil studies. For me, it was a perfect combination of studying natural phenomena’s and doing computations, both things I very much like to do. In my master’s thesis, I coded a coupled transport model into the existing numerical framework and even though I did not become a coder, I got a good idea of how numerical models work ‘from the inside’.

During the field work. Photos by Tailin Li.


During my Ph.D. studies I started to do more of field measurements, lab work, and in general, more experimental research. We studied the homogeneity of the subsoil with geophysical methods, how the topsoil topography changes due to tillage, and traffic affected the runoff generation. Also, a lot of work was done at our experimental catchment Nučice where we study the water dynamics in agricultural landscape (for more details see the post by Talin Li).  Most of the pits I dug in my life were during my Ph.D. while taking the soil samples or installing the probes.

In the SHui project I am part of the group dealing with WP 2.2 where we model soil and water movement in the landscape. One of the goals of our team is to assess various technical and agronomic control measures. My work specifically is to model the various agronomic practices under different climatic settings to show their effect on the water balance. Also, I was working quite closely with our Spanish colleges from the WP 2.1. The collaborative nature of the SHui project is one of the most valuable things for me.

In “Field Crops Research

Authors: YangLu, Tendai P.Chibarabada, Matthew F.McCabe, Gabriëlle J.M.De Lannoy, JustinSheffield


The application of crop models towards improved local scale prediction and precision management requires the identification and description of the major factors influencing model performance. Such efforts are particularly important for dryland areas which face rapid population growth and increasing constraints on water supplies. In this study, a global sensitivity analysis on crop yield and transpiration was performed for 49 parameters in the FAO-AquaCrop model (version 6.0) across three dryland farming areas with different climatic conditions. The Morris screening method and the variance-based Extended Fourier Amplitude Sensitivity Test (EFAST) method were used to evaluate the parameter sensitivities of several staple crops (maize, soybean or winter wheat) under dry, normal and wet scenarios. Results suggest that parameter sensitivities vary with the target model output (e.g., yield, transpiration) and the wetness condition. By synthesizing parameter sensitivities under different scenarios, the key parameters affecting model performance under both high and low water stress were identified for the three crops. Overall, factors relevant to root development tended to have large impacts under high water stress, while those controlling maximum canopy cover and senescence were more influential under low water stress. Parameter sensitivities were also shown to be stage-dependent from a day-by-day analysis of canopy cover and biomass simulations. Subsequent comparison with AquaCrop version 5.0 suggests that AquaCrop version 6.0 is less sensitive to uncertainties in soil properties.

Read the paper here.

In “Water

Authors: Francis Kilundu Musyoka, Peter Strauss, Guangju Zhao, Raghavan Srinivasan and Andreas Klik


The quantitative prediction of hydrological components through hydrological models could serve as a basis for developing better land and water management policies. This study provides a comprehensive step by step modelling approach for a small agricultural watershed using the SWAT model. The watershed is situated in Petzenkirchen in the western part of Lower Austria and has total area of 66 hectares. At present, 87% of the catchment area is arable land, 5% is used as pasture, 6% is forested and 2% is paved. The calibration approach involves a sequential calibration of the model starting from surface runoff, and groundwater flow, followed by crop yields and then soil moisture, and finally total streamflow and sediment yields. Calibration and validation are carried out using the r-package SWATplusR. The impact of each calibration step on sediment yields and total streamflow is evaluated. The results of this approach are compared with those of the conventional model calibration approach, where all the parameters governing various hydrological processes are calibrated simultaneously. Results showed that the model was capable of successfully predicting surface runoff, groundwater flow, soil profile water content, total streamflow and sediment yields with Nash-Sutcliffe efficiency (NSE) of greater than 0.75. Crop yields were also well simulated with a percent bias (PBIAS) ranging from −17% to 14%. Surface runoff calibration had the highest impact on streamflow output, improving NSE from 0.39 to 0.77. The step-wise calibration approach performed better for streamflow prediction than the simultaneous calibration approach. The results of this study show that the step-wise calibration approach is more accurate, and provides a better representation of different hydrological components and processes than the simultaneous calibration approach.
Read the paper here.

In “Soil and Tillage Research

Authors: M.López-Vicente, J.A.Gómez, G.Guzmán, J.Calero, R.García-Ruiz


Soil erosion plays an important role in C cycling at farm scale, especially in bare soil areas. In Mediterranean woody crops, temporary cover crops (CC) effectively reduce soil erosion and increase total and protected soil organic carbon (SOC) fractions. However, the effects of CC in olive groves on the preferential loss of organic carbon (Corg) fractions remains poorly understood. To address this issue, in four plots with seeded CC and two tilled plots (CT) in a Spanish olive grove, the unprotected and protected Corg fractions were measured in soil and sediments over the course of a hydrological year. The sediment/soil C enrichment ratios (ERSOC) were calculated, and results analysed considering the rainfall regimes of the site: dry (DS), heavy-rainy (HRS) and rainy (RS). Total, unprotected and protected Corg contents in the top 5 cm soil of CC plots were 46 %, 88.4 % and 28.5 %, respectively, higher than those of CT. 79.7 % and 70.3 % of the annual sediment yield (SY) was collected during December in CC and CT plots, respectively. Soil loss in CC plots ( = 9.2 Mg ha–1 yr–1) was significantly lower (−55.6 %) than that in CT plots. Despite that the average eroded Corg was higher in the CT ( = 222 kg C ha–1 yr–1) compared to CC ( = 148 kg C ha–1 yr–1) plots differences were not significant due to the higher Corg concentration in the sediment from CC plots. The highest proportion of eroded Corg (44%–45%) corresponded to the physically protected fraction. The highest ERSOC (1.99 and 2.04 for CC and CT, respectively) was recorded in DS whereas the lowest was in the RS (0.90) and HRS (0.96) seasons. The mean ERSOC were of 1.00 and 0.92 in the CC and CT plots, with no significant difference. The fact that most of the SY was recorded in one month, when CC plants were not fully developed, might explain the ERSOC at 1, and why their presence did not modify it. This study demonstrates that CC favours greater total, unprotected and protected Corg fractions in the topsoil, promoting soil C sequestration. The asynchrony between the periods of full development of the CC plants and those with the highest rainfall erosivity prevented any selectiveness of the eroded Corg. Thus, fast-growing CC plant species with short life-cycles are recommended, as well as adequate management to promote self-seeding avoiding soil disturbance for seeding in erosion prone seasons.
Read the paper here.

Author: Louise Busschaert, PhD candidate at KU Leuven

Water is precious and essential for optimal agricultural production. Its availability is threatened in a changing climate, and therefore irrigation is increasingly monitored. Even if irrigation is becoming more and more efficient, will it be enough? The first question we could address is: how much water will we need to sustain optimal crop production in the future? I started exploring this topic for the European continent as part of my MSc thesis at KU Leuven, while majoring in soils and water systems. Now, I am continuing this research as part of my PhD. How did I get there? During my BSc and MSc degree (in bioscience engineering), many courses focused on field and plant-scale processes. After acquiring the necessary knowledge about these small-scale processes, I learned to look at the bigger picture in other courses, in which they addressed soil and water problems at larger scales. This led to the choice of a MSc thesis subject in this direction.

I used the newly developed spatial version of AquaCrop, developed by Shannon de Roos (Work Package 3), and applied it to a concrete research question: ‘What are the future trends in net irrigation requirements in Europe?’. Basically, I used the spatial AquaCrop to ‘look into the future’. To this end, consistent future climate data was required. Having an idea of the uncertainty of future predictions is also a challenge. To meet these requirements, I used meteorological data from the Inter-Sectoral Intercomparison Project (ISIMIP). ISIMIP aims to provide consistent climate input datasets. The model, with this new input data, performs well by comparing AquaCrop estimations of surface soil moisture to various satellite products, proving the model can be used to estimate future net irrigation requirements.

I generated maps showing the evolution of net irrigation requirements depending on the future emission scenario. The values presented on the maps compare the future requirements (during June, July and August) to the requirements of a reference period. This change is called . Results are clear: under high emission scenarios, more water will be required in the future. Future climate will not only have an influence on future water amounts, but it will also alter the variability of irrigation requirements. Irrigation needs will vary highly from one year to another in countries such as France, Belgium, and Germany. A paper describing this research is expected to be published next year. I will also present my findings in a 3-minute lightning poster presentation at the AGU Fall Meeting 2021.

The different research aspects gathered in this research allowed me to work closely with the KU Leuven team, specialized in modeling and remote sensing, and also to collaborate with Prof. Wim Thiery from the VUB (Brussels) to get a better understanding of climate change scenarios. But most importantly, it ultimately opened doors to a new adventure for me, namely a PhD.

What’s next? AquaCrop, has been upgraded to a newer version (from version 6 to 7), enabling the use of more functionalities. A big novelty is the inclusion of perennial crops in the model. This new version has been successfully implemented in the spatial AquaCrop system. We are also working on implementing AquaCrop into a data assimilation system, hoping to improve model predictions. As part of my PhD, I will keep on contributing to these new tools.

In “Civil Engineering  Journal

Authors: Jakub Jeřábek, David Zumr


Catchment drainage area is a basic spatial unit in landscape hydrology within which the authorities estimate a water balance and manage water resources. The catchment drainage area is commonly delineated based on the surface topography, which is determined using a digital elevation model. Therefore, only a flow over the surface is implicitly considered. However, a substantial portion of the rainfall water infiltrates and percolates through the soil profile to the groundwater, where geological structures control the drainage area instead of the topography of the soil surface. The discrepancy between the surface topography-based and bedrock-based drainage area can cause large discrepancies in water balance calculation. It this paper we present an investigation of the subsurface media stratification in a headwater catchment in the central part of the Czech Republic using a geophysical survey method – electrical resistivity tomography (ERT). Results indicate that the complexity of the subsurface geological layers cannot be estimated solely from the land surface topography. Although shallow layers copy the shape of the surface, the deeper layers do not. This finding has a strong implication on the water transport regime since it suggests that the deep drainage may follow different pathways and flow in other directions then the water in shallow soil profile or shallow subsurface structures.
Read the paper here.

In “Science of The Total Environment

Authors: J.M.Ramírez-Cuesta, M.Minacapilli, A.Motisi, S.Consoli, D.S.Intrigliolo, D.Vanella


The identification and recognition of the land processes are of vital importance for a proper management of the ecosystem functions and services. However, on-ground land uses/land covers (LULC) characterization is a time-consuming task, often limited to small land areas, which can be solved using remote sensing technologies. The objective of this work is to investigate how the different MODIS NDVI seasonal parameters responded to the main land processes observed in Europe in the 2000-2018 period; characterizing their temporal trend; and evaluating which one reflected better each specific land process. NDVI time-series were evaluated using TIMESAT software, which extracted eight seasonality parameters: amplitude, base value, length of season, maximum value, left and right derivative values and small and large integrated values. These parameters were correlated with the LULC changes derived from COoRdination of INformation on the Environment Land Cover (CLC) for assessing which parameter better characterized each land process. The temporal evolution of the maximum seasonal NDVI was the parameter that better characterized the occurrence of most of the land processes evaluated (afforestation, agriculturalization, degradation, land abandonment, land restoration, urbanization; R2 from 0.67-0.97). Large integrated value also presented significant relationships but they were restricted to two of the three evaluated periods. On the contrary, land processes involving CLC categories with similar NDVI patterns were not well captured with the proposed methodology. These results evidenced that this methodology could be combined with other classification methods for improving LULC identification accuracy or for identifying LULC processes in locations where no LULC maps are available. Such information can be used by policy-makers to draw LULC management actions associated with sustainable development goals. This is especially relevant for areas where food security is at stake and where terrestrial ecosystems are threatened by severe biodiversity loss.

Read the paper here.


Authors: Barlin O.Olivares, JulioCalero, Juan C.Rey, Deyanira Lobo, Blanca B.Landa, José A.Gómez 


Soil morphological properties described in the field, such as texture, consistence or structure, provide a valuable tool for the evaluation of soil productivity potential. In this study, we developed a regression model between the soil morphological variables of banana plantations and a crop Productivity Index (PI) previously developed for the same areas in Venezuela. For this, we implemented categorical regression, an optimal scaling procedure in which the morphological variables are transformed into a numerical scale, and can thus be entered in a multiple regression analysis. The model was developed from data from six plantations growing “Gran Nain” bananas, each with two productivity levels (high and low), in two 4-ha experimental plots, one for each productivity level. Sixty-three A horizons in thirty-six soils were described using 15 field morphological variables on a nominal scale for structure type, texture and hue, and an ordinal scale for the rest (structure grade, structure size, wet and dry consistence, stickiness, plasticity, moist value, chroma, root abundance, root size, biological activity and reaction to HCl). The optimum model selected included biological activity, texture, dry consistence, reaction to HCl and structure type variables. These variables explained the PI with an R2 of 0.599, an expected prediction error (EPE) of 0.645 and a standard error (SE) of 0.135 using bootstrapping, and EPE of 0.662 with a SE of 0.236 using 10-fold cross validation. Our study showed how soil quality is clearly related to productivity on commercial banana plantations, and developed a way to correlate soil quality indicators to yield by using indicators based on easily measured soil morphological parameters. The methodology used in this study might be further expanded to other banana-producing areas to help identify the soils most suitable for its cultivation, thereby enhancing its environmental sustainability and profitability.

Read the paper here.

My name is Yingying Ma and I’m a PhD student, studying in College of Water Resources and Architectural Engineering, Northwest A&F University (China). My research focuses on cotton responses to salinity and deficit irrigation. In China, I’ve investigated how exogenous K+/Ca2+ application during fertigation enhances cotton tolerance to salt stress under different irrigation regimes. This research aims to provide useful information to better manage irrigation and fertilization to guarantee fibre yield in future water-limited and salinity-affected soil environments.

In August, 2020, I was awarded China Scholarship Council ( funding to sponsor one year of my studies at Lancaster University, UK. After vaccination, I was able to join the group in June 2021. My research here examines cotton responses to drought stress under various irrigation regimes, in aiming to determine fundamental mechanisms of how plant roots sense and translocate drought signals to the shoot to regulate leaf stomatal behavior and reduce luxury transpiration.

Images show Yingying Ma evaluating the split-root cotton plants to establish PRD experiments

Previously, Chinese researchers now working in the SHui consortium established that partial rootzone drying (PRD) induced favourable agronomic responses in cotton such as increased yields during the early harvests of the crop, which produced higher quality fibres for a better price (Tang et al. 2005). Although PRD caused greater stomatal closure than conventional irrigation when 30% less water was applied, more recent work by SHui researchers showed no effect of heterogenous salinity (analogous to PRD) on tomato canopy-level physiological response. Thus my research will use a whole plant gas exchange system (Jauregui et al. 2018) available at Lancaster to measure cotton seedling responses to deficit irrigation, to understand whether these techniques can be used to improve water use efficiency to sustain cotton production in a future water-limited areas of Europe and China.

Since arriving in Lancaster, I’ve met many fellow researchers and with their help learned to use instruments (e.g. Scholander pressure chamber to collect xylem sap) that I found difficult to use in China. I was able to attend the Delta-T training workshop to learn how to best use the porometer and various probes to measure stomatal conductance and soil moisture. Thanks to everyone’s generous help, I’ve learned many methods to help my current research and data analysis which have made my research more efficient. I am very grateful to Prof Ian Dodd as my host, and the Chinese Scholarship Council and SHui project supporting me at the Lancaster Environment Centre.