by Rastislav Skalsky, Ecosystems Services and Management Program, International Institute for Applied Systems Analysis
As growers, we know soil is important. It supports plants, and provides nutrients and water for them to grow. But do we all appreciate how crucial the role of soil is in continuously supplying plants with water, even when it hasn’t rained for a few days or even weeks, even without extra water being added via watering?
Soil is like a sponge. It can retain rain water and, if it is not taken up by plants, soil can store it for a long time. We can feel the water in soil as soil moisture. Try it — take and hold a lump (clod) of soil — if it is wet it will leave a spot on your palm. If it’s only moist then it will feel cold — cooler than the air around. And if the soil is dry, it will feel a little warm.
Soil moisture not only can be felt, but it can also be measured — in the lab, or directly in the field with professional or low-cost soil moisture sensors.
Soil moisture in general indicates how much water is contained by the soil. But it is not always the case that soil which feels moist or wet is able to support plants. It could happen that, despite feeling moist, the soil simply does not hold enough water, or holds the water too tightly for the plants to extract it. Or the opposite, soil can sometimes contain too much water. To understand how this works, one has to learn more about how water is stored in the soil.
Water is bound to soil by physical forces. Some forces are too weak to hold water in the plant root zone and water percolates to deeper layers, where plants can no longer reach it. Other forces can be too strong, preventing water from being retrieved by the roots.
If soil moisture is measured at one place over time, it can reveal its seasonal dynamics. Having estimated important soil water content thresholds (FS — full saturation, FC — field capacity, PDA — point of decreased availability, and WP — wilting point) for that particular site, e.g. based on soil texture test or measurement, one can easily interpret if the measured soil moisture and say if there was enough water or not to fully support plants with water and air. In this particular case of sandy 0–30 cm deep topsoil from Slovakia, it was never wet enough to cause oxygen stress for plants, — in fact it never reached state of all capillary voids filled with water (FC). On the other hand, each summer the topsoil moisture dropped below the point of decreased availability (PDA), even got close to the wilting point or went through (WP), which means that during those periods plants suffered drought conditions.
Thresholds In order to describe this behavior in more useful terms, plant ecologists and soil hydrologists came up with couple of important soil water content thresholds (Figure 1). These thresholds, also called “soil moisture ecological intervals”, define how easily plants can get the water out of the soil.
We speak about full saturation of soil when all empty spaces (pores/voids) are completely filled with water. Full saturation of the soil with water prevents air entering into the soil. Yet there is no force holding water in the soil. Roots need air as well as water so, if this situation continues, it eventually causes oxygen stress for most of the common plants because roots simply cannot breathe.
Soil also has different types of pores. Larger ones, which are called “gravitational pores”, are filled with water only when the soil is saturated and otherwise drains freely, and smaller ones called “capillary pores” which are small enough in size to prevent water from percolating down the soil profile by gravitation. These smaller pores can hold water even in well-drained soils and make it available for plants to extract. There are also even smaller pores where the water is held so tightly that plants cannot extract it.
When all gravitational pores/voids are empty of water and it is present only in so called capillary pores/void we speak about the field water capacity — which is considered to be the best soil moisture status of the soil — enabling plants to retrieve the water they need, whilst leaving enough air for roots to breathe. If no new water is added into the soil, the soil dries as water is used by plants or evaporates. As soil dries less water is available to plants until the point of decreased availability when water remains only in the smallest capillary pores/voids. But this water is bound to soil particles so strongly that most plants are not able to extract it suffer from drought. Ultimately, all the available water is used up by plants, and the remaining water is inaccessible. Soil reaches the so-called wilting point and water is not available for the plants anymore. Plants permanently wilt and eventually die.
How Soil Characteristics Relate to Moisture The tricky thing with soil moisture however is that the same amount of water (volumetric percent of the total soil column volume) can, in different soils, represent different amount of water available for plants. How big this difference could be is defined by many soil characteristics.
The most important is the soil texture — a blend of all fine-earth soil mineral constituents (sand, silt, clay) and stones in various rates. In general, the finer the texture is (i.e. more clay, less sand) the more water is bound in the soil too tightly to be retrieved by plants. Even if the soil feels moist, plants can permanently wilt in clay soils. In contrast, those soils with coarse texture (i.e. more sand, less clay) can support plants with nearly all the water they can hold. Although the soil looks dry, plants can still effectively take the water out of it. The drawback here is that in coarse textured, sandy, soil nearly all water drains down the gravitational pores and therefore such a soil cannot support plants for very long time. That is also why medium textured soils (loam, silty loam, clay loam) are considered best for holding and providing the water for plants. Medium textured soils can effectively drain excess water, yet hold much water in capillary pores/voids for a long time, and still, only a relatively small amount of water remains unavailable for the plants.
A practical implication of this behavior of soil with different soil texture could be that one has to apply slightly different strategies to maintain soil moisture in the way that it can effectively supply plants with water. Sandy soils will require more frequent watering with smaller amount of water. It would not make any practical sense to try build-up a storage of water in these soils. All extra water added will simply drain out of the topsoil. Clay rich soils can absorb big amounts of water but a lot is bounded too strongly to the soil particles and thus not available for the plants. Therefore one should water even if the soil looks moist or wet — and if dry a lot of water must be added to recharge the topsoil so that it can support plants effectively. With loamy soils it is possible to be more relaxed with watering frequency, simply because one can build solid storage of water in such soils. Adding a bit more water than is necessary is perfectly fine with these soils because the water is effectively kept in the soil profile and it can be used later on.
Interested in learning more? Why not sign up for GROW Observatory’s next free online course – Citizen Research: From Data to Action – to discover how citizen-generated data on soils, food and a changing climate can create positive change in the world. Starts 5th November.
By Marcus Thomson, researcher, IIASA Ecosystems Services and Management Program
While living in Cairo in 2010, I witnessed first-hand the human toll of political and environmental disasters that washed over Africa at the end of the last century. Unprecedented numbers of migrants were pressing into North Africa, many pushed out of their homelands by conflict and state-failure, pulled towards safer, richer, less fragile places like Europe. Throughout Sub-Saharan Africa, climate change was driving up competition for scarce land and water, and raising pressure on farmers to maintain the quantity and quality of their crops.
It is a similar story throughout the developing world, where many farmers do without the use of expensive chemical fertilizer and pesticides, complex irrigation, or boutique seed varieties. They rely instead on traditional land management practices that developed over long periods with consistent, predictable conditions. It is difficult to predict how dryland farmers will respond to climate change; so it is challenging to plan for various social, economic, and political problems expected to develop under, or be exacerbated by, climate change. Will it spur innovation or, as has been argued for the Syrian civil war, set up conflict? A major stumbling block is that the dynamics of human social behavior are so difficult to model.
Instead of attempting to predict farmers’ responses to climate change by modelling human behavior, we can look to the responses to environmental changes of farmers from the past as analogues for many subsistence farmers of the future. Methods to fill in historical gaps, and reconstruct the prehistoric record, are valuable because they expand the set of observed cases of societal-scale responses to environmental change. For instance, some 2000 years ago, an expansive maize-growing cultural complex, the Ancestral Puebloans (APs), was well established in the arid American Southwest. By AD 1000, members of this AP complex produced unique and innovative material culture including the famed “Great Houses”, the largest built structures in the United States until the 19th century. However, between AD 1150 and 1350, there was a profound demographic transformation throughout the Southwest linked to climate change. We now know that many APs migrated elsewhere. As a PhD student at the University of California, Los Angeles, I wondered whether a shift to cooler, more variable conditions of the “Little Ice Age” (LIA, roughly AD 1300 to 1850) was linked to the production of their staple crop, maize.
I came to IIASA as a YSSP in 2016 to collaborate with crop modelers on this question, and our work has just been published in the journal Quaternary International. I brought with me high-resolution data from a state-of-the-art climate model to drive the crop simulations, and AP site information collected by archaeologists. Because AP maize was quite different from modern corn, I worked with IIASA soil scientist Juraj Balkovič to modify the crop simulator with parameters derived from heirloom varieties still grown by indigenous peoples in the Southwest. I and IIASA economic geographer Tamás Krisztin developed a statistical technique to analyze the dynamical relationship between AP site occupation and simulated yield outcomes.
We found that for the most climate-stressed high-elevation sites, abandonments were most associated with increased year-to-year yield variability; and for the least stressed low-elevation and well-watered sites, abandonment was more likely due to endogenous stressors, such as soil degradation and population pressure. Crucially, we found that across all regions, populations peaked during periods of the most stable year-to-year crop yields, even though these were also relatively warm and dry periods. In short, we found that AP maize farmers adapted well to gradually rising temperatures and drought, during the MCA, but failed to adapt to increased climate variability after ~AD 1150, during the LIA. Because increased variability is one of the near certainties for dryland farming zones under global warming, the AP experience offers a cautionary example of the limits of low-technology adaptation to climate change, a business-as-usual direction for many sub-Saharan dryland farmers.
This is a lesson from the past that policymakers might take note of.
 Kelley, C. P., Mohtadi, S., Cane, M. A., Seager, R., & Kushnir, Y. (2015). Climate change in the Fertile Crescent and implications of the recent Syrian drought. Proceedings of the National Academy of Sciences, 201421533.
 Thomson, M. J., Balkovič, J., Krisztin, T., MacDonald, G. M. (2018). Simulated crop yield for Zea mays for Fremont Ancestral Puebloan sites in Utah between 850-1499 CE based on temperature dailies from a statistically downscaled climate model. Quaternary International. https://doi.org/10.1016/j.quaint.2018.09.031
By Sandra Ortellado, 2018 Science Communication Fellow
China is the world’s biggest producer of both wild and farmed fish, yet the massive commercial fishing industry threatens thousands of years of tradition in ocean and freshwater fishing, as well as the livelihoods of coastal fishing communities.
In the past decade, some coastal ecosystems and environments have been destroyed and polluted in the process of industrialization. Millions of tons of fish are caught in Chinese territorial waters each year, such that overfishing of high value commercial species has led to a drastic decline of some native fisheries resources and species.
In response, the Chinese government released a five-year plan for protecting marine ecosystems and restoring wild capture fisheries. The plan promotes an agenda of “ecocivilization,” which emphasizes land–sea coordination, green development, and social–ecological balance.
It also calls for the introduction of additional output control measures, which directly limit the amount of fish coming out of a fishery. Existing input control measures restrict the intensity of gear used to catch fish, but they may not be sufficient to protect ecosystems.
Yi Huang, a member of this year’s YSSP cohort, has made it her goal to figure out how social ecological balance can be achieved even as fishery regulations shift towards increased input and output control.
Given the size of China’s fishing industry, large scale change requires the abstract concepts of “ecocivilization” to be translated into action, compliance, and enforcement at the local level. That means engaging with individual fishers, their communities, and their way of life, says Huang.
“If you want to control overfishing; the object of fishery management policy are fishers. So you need to understand human behavior to help you control overfishing.”
Huang’s project investigates how changes in fishery management will affect demographic, geographic, and socioeconomic trends in the Chinese fisher population. With the guidance of her supervisors, she’s also developing a bioeconomic model to analyze how output control measures like catch limits will affect ecological and socioeconomic conditions.
“I just want to figure out how to improve enforcement of this kind of policy and see if we can use it to solve the overfishing problem at the same time as giving those in the fishing industry a better life,” says Huang.
Current input control measures like licensing systems, vessel buyback programs, closed seasons, restricted areas, and fisher relocation programs are meant to discourage overfishing and transition towards more sustainable practices. Nevertheless, a decline in fishing vessels and restricted fishing seasons only resulted in an increase in total vessel engine power and large spikes in fishing activity just prior to the closed season.
According to the Chinese fishery statistical yearbook, the number of people employed in the fishing industry proliferated to 13.8 million in 2016, so in recent years the government has issued subsidies encouraging fishers not to fish in the off-season and to change vocation. Older fishers are hesitant to abandon an identity that has been passed down from generation to generation in their families. However, younger generations with access to higher education are lured by the prospect of more stable work outside of their fishing communities, which could really change the socioeconomic and demographic structure of coastal villages.
With the potential for increased output controls to incur drastic changes in coastal communities, it’s more important than ever that regulations are carefully designed with both socioeconomic and ecological factors in mind.
Huang hopes her research will help inform the process of policy development, which involves balancing the needs of both vulnerable fisheries labor and delicate ecosystems.
“When policymakers want to use output control in fishery management, maybe they will think more about the fisher or the socioeconomic aspect of the resolution,” says Huang.
“My research is at the national level, but when they design a regulation it’s at the local level, so my research can teach them how socioeconomic surveys at the local level can be used together with ecological research when they are preparing for regulations.”
Huang, who studied sociology at the Ocean University of China before starting as a PhD student in Marine Affairs at Xiamen University, has spent the past ten years researching coastal fishing communities. She has a deep fondness for the people she surveyed, who welcomed her into their homes and showed her the beauty of the environment that sustained them.
“I want to protect the ocean and the people that connect with it,” says Huang.
A sociological perspective has given Huang an eye for nuance and an appreciation for things that don’t turn out quite how you expect, as they often don’t in scientific research—especially when it attempts to explain human behavior.
For example, Huang says that although fishers may look like countryside people, they act very differently from farmers.
“The ocean has a lot more risk involved than planting on land,” explains Huang.
Because Chinese fishery regulation is currently focused almost exclusively on analyzing resources from an ecological perspective, she thinks sociology and anthropology research could add another revealing dimension to the approach.
“After doing surveys and analyzing the data, I will find maybe I’m wrong or maybe there is something more. That’s why I’m really interested in this kind of research,” says Huang.
As her research project develops, Huang says she’s grateful for the feedback of her supervisors and peers at IIASA, who both challenge and encourage her.
“Even when they have some critical comments on my research, I feel more confident that my research is meaningful, that they support me, and that they’re really interested in my research,” says Huang. “That’s what I can feel every day.”
By Valeria Javalera Rincón, IIASA CONACYT Postdoctoral Fellow in the Ecosystems Services and Management and Advanced Systems Analysis programs.
What is more important: water, energy, or food?
If you work in the water, energy or agriculture sector we can guess what your answer might be! But if you are a policy or decision maker trying to balance all three, then you know that it is getting more and more difficult to meet the growing demand for water, energy, and food with the natural resources available. The need for this balance was confirmed by the 17 Sustainable Development Goals, agreed by 193 countries, and the Paris climate agreement. But how to achieve it? Intelligent cooperation is the key.
The thing is that water, energy, and food are all related in such a way that are reliant on each other for production or distribution. This is the so-called Water-Energy-Food nexus. In many cases, you need water to produce energy, you need energy to pump water, and you need water and energy to produce, distribute, and conserve food.
Many scientists have tried to relate or to link models for water, agriculture, land, and energy to study these synergic relationships. In general, so far, there are two ways that this has been solved: One is integrating models with “hard linkages” like this:
In the picture there are six models (let’s say water, land use, hydro energy, gas, coal, food production models) that are then integrated into just one. The resulting integrated model then preserves the relationships but is complex, and in order to make it work with our current computer power you often have to sacrifice details.
Another way is to link them is using so-called “soft linkages” where the output of one model is the input of the next one, like this:
In the picture, each person is a model and the input is the amount of water left. These models all refer to a common resource (the water) and are connected using “soft linkages.” These linkages are based on sequential interaction, so there is no feedback, and no real synergy.
The intelligent linker agent
But what if we could have the relations and synergies between the models? It would mean much more accurate findings and helpful policy advice. Well, now we can. The secret is to link through an intelligent linker agent.
I developed a methodology in which an intelligent linker agent is used as a “negotiator” between models that can communicate with each other. This negotiator applies a machine-learning algorithm that gives it the capability to learn from the interactions with the models. Through these interactions, the intelligent linker can advise on globally optimal actions.
When I came to IIASA, I was asked to apply this approach to optimize trading between cities in the Shanxi region of China. I used a set of previously development models which aimed to distribute water and land available for each city in order to produce food (eight types of crops) and coal for energy. The intelligent linker agent optimizes trading between cities in order to satisfy demand at the lowest cost for each city.
The purpose of this exercise was to compare the solutions with those from “hard linkages” – like those in the first picture. We found that the intelligent linker is flexible enough to find the optimal solution to questions such as: How much of each of these products should each city export/import to satisfy global demand at a global lower economic and ecological cost? What actions are optimal when the total production is insufficient to meet the total demand? Under what conditions is it preferable to stop imports/exports when production is insufficient to supply the demand of each city?
The answers to these questions can be calculated by the interaction with the models of each city just by the interfacing with the intelligent linker agent, this means that no major changes in the models of each city were needed. We also found that, under the same conditions, the solutions using the intelligent linker agent were in agreement with those found when hard linking was used.
My next challenge is to build a prototype of a “distributed computer platform,” which will allow us to link models on different computers in different parts of the world—so that we in Austria could link to a model built by colleagues in Brazil, for example. I also want to link models of different sectors and regions of the globe, in order to prove that intelligent cooperation is the key to improving global welfare.
Javalera V, Morcego B, & Puig V, Negotiation and Learning in distributed MPC of Large Scale Systems, Proceedings of the 2010 American Control Conference, Baltimore, MD, 2010, pp. 3168-3173. doi: 10.1109/ACC.2010.5530986
Valeria J, Morcego B, & Puig V, Distributed MPC for Large Scale Systems using Agent-based Reinforcement Learning, In IFAC Proceedings Volumes, Volume 43, Issue 8, 2010, Pages 597-602, ISSN 1474-6670, ISBN 9783902661913, https://doi.org/10.3182/20100712-3-FR-2020.00097.
Morcego B, Javalera V, Puig V, & Vito R (2014). Distributed MPC Using Reinforcement Learning Based Negotiation: Application to Large Scale Systems. In: Maestre J., Negenborn R. (eds) Distributed Model Predictive Control Made Easy. Intelligent Systems, Control and automation: Science and Engineering, vol 69. Springer, Dordrecht
Javalera Rincón V, Distributed large scale systems: a multi-agent RL-MPC architecture, Universitat Politècnica de Catalunya. Institut d’Organització i Control de Sistemes Industrials,Doctoral thesis. 2016. http://upcommons.upc.edu/handle/2117/96332
Note: This article gives the views of the author and not the position of the Nexus blog, nor of the International Institute for Applied Systems Analysis.
The public can contribute considerably to science by filling the gaps of missing information in many research areas, for example, monitoring land use, biodiversity, or forest degradation. Crowdsourcing campaigns organized by research institutions bring together citizens interested in science and help solving research questions to the benefit of the whole world.
This June, the IIASA Geo-Wiki team ran the Global Field Size campaign, encouraging citizen scientists to classify field sizes on satellite images. Its aim was to develop a global field sizes dataset, which will be used as input to create an improved global cropland field size map for agricultural monitoring and food security assessments. The field sizes dataset can also help us determine what types of satellite data are needed for agricultural monitoring in different parts of the world.
Geo-Wiki interface for collecting field size data. Background layer: Google Maps.
Why are field sizes so important? They provide us with valuable information to tackle challenges of food security. A recent study showed that more than a half the food calories produced globally comes from smallholder farmers, who often make up the most vulnerable parts of population, living in poverty. Within this scope, the field size dataset fills the gaps of missing information, especially for countries that have a limited food supply and lack a well-developed agricultural monitoring system.
The Global Field Size campaign has been one of the most successful crowdsourcing campaigns run through the Geo-Wiki engagement platform. Within one month, 130 participants completed 390,000 tasks – that is, they classified the field sizes in 130,000 locations around the globe!
So we can see that crowdsourcing is powerful, but can we trust the data? Is it accurate enough to be used in different applications? I think it is! The Geo-Wiki team has significant experience in running crowdsourcing campaigns; one of the key lessons we have learned from previous Geo-Wiki campaigns is the importance of training the public to increase the quality of the crowdsourced data.
This campaign was designed so that the participants learned over time how to delineate fields in different regions of the world, and, at the same time, pay special attention to the quality of their submissions. At the end of the campaign, the majority of participants gave us a feedback that, to them, this campaign was indeed a learning exercise. From our end, I have to add, this was also a challenging campaign, as fields are so diverse in shape, continuity of coverage, crop type, irrigation, etc.
Global distribution of dominant field sizes. Cartography by Myroslava Lesiv. Country boundaries: GAUL. Software: ArcMap 10.1.
During the campaign, the crowd was asked to identify whether there were fields in a certain location, and determine the relevant field sizes by the visual interpretation of very high-resolution Google and Bing imagery. A “field” was defined as an agricultural area that included annual or perennial croplands, fallow, shifting cultivation, pastures or hayfields. The collected data can also be used to identify areas falsely mapped as cropland.
Now the team is focused on summarizing the results of the campaign, processing the collected field size data, and preparing them for scientific publication. We will ensure that the published dataset is of high quality and can be used by others with confidence!
This article gives the views of the author, and not the position of the Nexus blog, nor of the International Institute for Applied Systems Analysis.
By Victor Maus, IIASA Ecosystems Services and Management Program
Nowadays, satellite images are an abundant supply of data which we can use to get information about our planet and its changes. Satellite images can, for example, help us detect an approaching storm, measure the expansion of a city, identify deforested areas, or estimate how crop areas change over time. Usually, we are interested in extracting information from large areas, for example, deforestation in the Amazon Rainforest (5.5 million km², around 15 times the area of Germany). It would be challenging for us to monitor and map such vast areas without combining satellite images with automated and semi-automated computer programs.
To address this problem, I developed — along with my colleagues Gilberto Camara from the Brazilian National Institute for Space Research and Marius Appel and Edzer Pebesma from the University of Münster, Germany — a new open source software to extract information about land-cover changes from satellite images. The tool maps different crop types (e.g., soybean, maize, and wheat), forests, and grassland, and can be used to support land-use monitoring and planning.
Our software, called dtwSat, is open-source and can be freely installed and used for academic and commercial purposes. It builds upon on other graphical and statistical open-source extensions of the statistical program R. Adding to that, our article in press in Journal of Statistical Software is completely reproducible and provides a step-by-step example of how to use the tool to produce land-cover maps. Given that we have public access to an extensive amount satellite images, we also get much benefit from tools that are openly available, reproducible, and comparable. These, in particular, can contribute to rapid scientific development.
The software dtwSat is based on a method widely used for speech recognition called Dynamic Time Warping (DTW). Instead of spoken words, we adapted DTW to identify ‘phenological cycles’ of the vegetation. These encompass the plants’ life cycle events, such as how deciduous trees lose their leaves in the fall. The software compares a set of phenological cycles of the vegetation measured from satellite images (just like a dictionary of spoken words) with all pixels in successive satellite images, taken at different times. After comparing the satellite time series with all phenological cycles in the dictionary, dtwSat builds a sequence of the land-cover maps according to similarity to the phenological cycles.
The series of maps produced by dtwSat allows for land-cover change monitoring and can help answer questions such as how much of the Amazon rainforest has been replaced with soy or grass for cattle grazing during the last decade? It could also help study the effects of policies and international agreements, such Brazil’s Soy Moratorium, where soybean traders agreed not to buy soy from areas deforested after 2006 in the Brazilian Amazon. If soy farming cannot expand over areas deforested after 2006, it might expand to areas formerly used for cattle grazing deforested before 2006, and force the cattle grazing farmers to open new areas that have been cleared more recently. Therefore, besides monitoring changes, the land-cover information can help better understand direct and indirect drivers of deforestation and support new land-use policy.
Maus, V, Camara, G, Cartaxo, R, Sanchez, A, Ramos, FM, & de Queiroz, GR (2016). A Time-Weighted Dynamic Time Warping Method for Land-Use and Land-Cover Mapping. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 9 (8): 3729–39.
This article gives the views of the author, and not the position of the Nexus blog, nor of the International Institute for Applied Systems Analysis.