Author: Shannon de Roos, PhD student at KU Leuven

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 journal Geoscientific 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.

Author: Nina E Noreika

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.

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.

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.

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.

Sustaining food production in the years ahead requires technically efficient management of natural resources and the surrounding environment. Different indicators have been suggested to measure the progress towards more efficient and productive agricultural systems. The yield gap (YG) is an important indicator in this context as it provides a benchmark for agricultural productivity. Yield gaps are defined as the difference between the potential (Yp) or water-limited yield (Yw) and the actual yield (Ya), in case of irrigated or rainfed cropping systems, respectively. Traditionally, these have been assessed with crop simulation models considering the most representative biophysical conditions and, to a lesser extent, crop management practices observed in farmers’ fields.

The extraordinary advances in computer engineering and programming languages, particularly over the last three decades, have intensified the modelling processes contributing to an increased adoption of such tools for many applications in agronomy. However, recent advances have not yet succeeded to scale up mechanisms from point to field level in crop models. In fact, while significant advances have been made in the engineering aspects of precision agriculture, such as increasing spatial resolution, variable rate technologies and automation, much less effort has been devoted to understand the crop mechanisms in response to spatial variations.

As considerable spatial variability in soil hydraulic properties exists within a field, even in those considered homogeneous, the accurate modelling of crop heterogeneity requires assessing the spatial variability of water as it affects crop behaviour. Essentially, if crop models are to be used to improve water management in precision agriculture, they may greatly benefit from spatial water modelling approaches capable of accurately represent and simulate within-field variation of water-related processes.

With the widespread advances in yield monitoring, and the suitable equilibrium between temporal and spatial resolution of freely available remote sensing data, there is potential to simulate spatial variations in crop performance with a fine-resolution for various crop types, and use it for management applications in precision agriculture. Yield mapping, which has been increasingly adopted by farmers in modern agriculture (mostly in cases of cereals or other important field crops), provides valuable spatial information on how crops perform in response to a certain gradient of Environmental (E) conditions and/or Management (M) practices. Within the same field, and for a particular growing season, the spatial variation of E (e.g., soil texture, soil water holding capacity and depth, pH and soil nutrient availability) and M (e.g., sowing rate and date, fertilization and crop protection) determine what can be understood as a ‘short-term’ phenotypic response (P) to variable conditions in combination with the grown genotype (G). When analyzing historical yield maps of uniformly managed fields, the GxExM interactions can be simply understood as direct responses of GxE interactions. Such interactions are not merely unidirectional, but reciprocal. The effect of GxE over P determines feedback mechanisms that do also affect the observed E gradient over a single field (e.g., increased vegetative vigor, as a function of higher water availability during crop growth stages, may enhance water stress during grain filling). The spatial variation of crop performance (P=GxE), when quantified in terms of grain yield, is in fact the result of multi-directional relations along the vector of interactions that links P to G and E for a single field and year. Precision agriculture depends on such theoretical considerations, as the first step towards site-specific management is the quantification of crop variations over space and time (Ya). Yield maps provide us one of the most pragmatic and technically oriented sorts of data to pursuit it.

Many of these challenges/opportunities are currently being addressed within SHui project by both the Institute for Sustainable Agriculture (IAS-CSIC) and the department of Agronomy of the University of Córdoba, in collaboration with other ‘working-package 2’ (WP2) members. In the Iberian peninsula for instance, where more than 2.4 M ha of winter cereals are rainfed grown every year, farmers have been collecting increasing amounts of data, which have great value to link crop simulation models with YG analysis to be conducted at spatially variable scales.

 My name is Tomás Roquette Tenreiro, I am an agronomist from Portugal. I have a degree in agronomy and agricultural engineering from the University of Lisbon, Portugal, and a master’s degree in crop science from the University of Wageningen, in the Netherlands. Currently, I am working as a predoctoral researcher in IAS-CSIC within Shui and I am also a young-farmer in the region of Alentejo, Portugal. Both as a researcher and a farmer, I find great value in applied research with the context of spatial heterogeneity assessment. Spatial heterogeneity is a reality in our conditions that enhances risks for both farmers and society. Risks of adopting ineffective measures, risks of dealing with non-representative data, risks of under/over-using resources, risks of unstable food supply, risks of economic losses and environmental damage. As a SHui researcher and a PhD candidate, most of my work is focused on closing the gaps between modelling tools and the main ‘spatial phenomena’ governing crop performance in local conditions. In this sense, I am actively collaborating with local farmers in the region of Córdoba, conducting field experimentation at “real scales” and dealing with on-farm data within most of my research.

I am currently responsible for a case-study within Shui-WP2, that is supervised by Prof. Elías Fereres and Dr. José Alfonso Gómez (also co-promoted by Dr. Margarita García-Vila). Our case-study integrates spatial water modelling with crop simulation (i.e., AquaCrop model) in order to assess spatial variations of YG’s and to explore precision agriculture strategies to deal with YG’s variations in space and time. Within this task, we are actively collaborating with the local company ‘Cortijo La Reina’, other individual farmers in the Guadalquivir Valley, Spain, and the Technical University of Prague, who is supporting us with the hydrological modelling. At present date, 1000 ha of historical yield maps and related management data have been assessed, in combination with field experimentation, conducted in two independent local fields, respectively of 10 ha (2019/20) and 8 ha (2020/21).

Local yields show coefficients of variation of 16-20% (within field) which are, among other factors, partially explained by water spatial variations. Within this context, we are exploring the adoption of variable rate technologies in N-P-K applications to close YG’s in different (water-availability) zones (within field). Multiple economic trade-offs exist in our conditions, varying from field to farm scales, but we believe that most of our research outcomes will support better decision making within the context of (rainfed) precision agriculture.

Further reading

– Tenreiro, T. R., García-Vila, M., Gómez, J. A., Jiménez-Berni, J. A., & Fereres, E. (2021). Using NDVI for the assessment of canopy cover in agricultural crops within modelling research. Computers and Electronics in Agriculture, 182, 106038.

– Tenreiro, T. R., García-Vila, M., Gómez, J. A., Jimenez-Berni, J. A., & Fereres, E. (2020). Water modelling approaches and opportunities to simulate spatial water variations at crop field level. Agricultural Water Management, 240, 106254

– Tenreiro, T. R., García-Vila, M., Gómez, J. A., & Fereres, E. (2020). From point to field scale – uncertainties associated with the upscaling of modelling for spatial heterogeneity assessment. In European Society of Agronomy 2020 Congress Book of Abstracts (p. 36). DOI 10.13140/RG.2.2.11611.80161

– Tenreiro, T. R., García-Vila, M., Gómez, J. A., & Fereres, E. (2020). Uncertainties associated with the delineation of management zones in precision agriculture. In EGU General Assembly Conference Abstracts (p. 5709).

– Tenreiro, T. R. & Fereres, E. (2019). Modelling Seminar on AquaCrop at Fuzhou – Fujian Agriculture University [digital.csic.es].