Modelling Spotlight on Sustainable Agriculture: SOL-m

Source: www.sojourneyfarm.com

Before we conclude the impact of agriculture on the environment (and say a hearty farewell to 2015!), I will quickly enlighten you with a dash of the modelling of sustainable agriculture, in particular, Sustainability and Organic-Livestock Modelling (SOL-m). The research of this study was published by the Food and Agriculture Organisation in 2013, and was designed to assess the environmental impacts of converting current livestock production systems into smaller, less resource intensive ones, with sustainable management of organic materials.

The model itself compares the effects of multiple production scenarios on land use and degradation, greenhouse gas emissions and global warming potential, nutrient flow, availability of food, consumption of fossil fuels, impact on biodiversity and more. It was constructed using conditional projections for food supply, food demand, and their interaction over time, by assessing available resources, land, human population, nutritional requirements and consumer choices. From here, environmental and social policy was also taken into consideration, and used linear programming to optimise each production scenario with respect to certain targets. 

The different scenarios investigated within the context of this study were:

• Scenario 1: baseline for 2050 (as predicted by FAO), alongside projections for population rate, dietary trends, expected yields, etc. Livestock feed was assumed to be remain consistent.
• Scenario 2: modelled a 50% decrease in concentrated feed, and measured number of livestock sufficient to give at least as many calories as Scenario 1.
• Scenario 3: the same as Scenario 2, but assumes no consumption of concentrates in livestock feed.
• Scenario 4: predicted conversion to organic livestock production, including organically produced feed concentrates. Again, measuring livestock to give at least as many calories as Scenario 1.
• Scenario 5: a combination of Scenarios 3 and 4 - a complete conversion to organic livestock farming, whilst omitting all concentrate in livestock feed.

The results concluded that Scenario 1 could not sustain a planet projected to reach a population of 9.6 billion by 2050 whilst still maintaining quality in the environment. In Scenarios 2 and 3, the model calculated substantial increases in food availability and security, whilst reducing environmental damage caused by deforestation and land degradation. This trend improved with higher reduction in livestock feed concentrates. Interestingly, although Scenario 4 measured significant decreases in greenhouse gas emissions and toxic material flow, it also predicted a lack of available food implying a need for more agricultural land (a finite resource). However, Scenario 5 yielded the best result, showing a positive results across the majority of environmental effects measured, suggesting that organic farming and diminished concentrate in livestock feed are two major factors necessary to achieve sustainable agriculture.

Industrial Crop Production: Part 2 - Models Galore

Source: www.organicfacts.net/

Models are fundamental tools to helping us understand and predict real-life scenarios without having to meddle and tamper with them in actuality. Crop modelling is of particular importance to our society, particularly when considering the global population’s high dependence on the security, efficiency and existence of crops. Additionally, the large scale of systems like these raises a multitude of potential environmental implications that must be measured carefully and projected as accurately as possible.

However, there is some ambiguity and scepticism revolving around the use of models, especially within the field of crop modelling. In the last two decades, research has be launched to reduce the uncertainty prevalent in existing models through inter-comparison, scaling, development of code, removal of deficiencies and calibration of models to suit certain regions more effectively.

Below are some strong examples of models, or improvement projects, which have been utilised in last 15 years (and are, quite possibly, the cream of the crop!).

SIRIUS

Sirius is a simulation model which focuses on a specific cereal, wheat, to estimate the growth of wheat in multiple conditions, including varying climate scenarios. Within the simulation, photosynthetically active radiation (PAR) (the wavelength range of photosynthesis, between 400nm to 700nm) is obtained, using satellite imaging, to measure the biomass, and net growth of wheat. The leaf area index (LAI) (the proportion of ground area covered by vegetation) is then found using a thermal-time model, in order to estimate the natural cycle of leaf appearance and fluctuation in wheat. From here, Sirius has been programmed to measure the nitrogen and water deficiencies in the crop, by combining observations of LAI over time with photosynthetic effect. The model has been successfully calibrated in Europe, New Zealand, Australia and USA, and has shown accurate results in the face of climate change.

ATOPICA

The purpose of ATOPICA is to model the health risks and future trends of a particular allergenic pollen strain “Ambrosia” associated with the European influence of climate change, pollution and land-use changes. The aims of the project are:

• Increase understanding of the interaction between environmental change and effects on sufferers of Ambrosia pollen-related allergies
• Assessing the present and future risks associated with allergens
• To formulate responses to outbreaks, and improve regulation, in a policy-making context

Source: www.atopica.eu/

ADAPTAWHEAT

ADAPTAWHEAT is a four year project, set to complete next month, after commencing in January 2012. Its aim is to improve crop yield and performance of wheat, and develop adaptation strategies against climate change, within Europe, by looking at particular genetic components and germplasm, for flowering time and its periodicity (phenology).

Source:www.rothamsted.ac.uk

LARS-WG

LARS-WG conducts a time-series analysis of the weather in certain locations to assess the risk of weather on agriculture and hydrology, and make future predictions based on climate change scenarios. Version 5.0 of the LARS-WG model uses data from 15 Global Climate Models (commonly known as GCMs) to eliminate or reduce uncertainty in results which can stem from differences in model design. Examples of the weather measured include heavy precipitation, drought and heat waves, alongside normal everyday weather activity.

AgMIP

AgMIP, the Agricultural Model Intercomparison and Improvement Project, has the core objective to inter-compare multiple models, reduce uncertainty and calibrate them sufficiently, so that these crop models output more reliable results for future projects of baseline and other climate change scenarios. These predictions will be combined with economic models, to formulate appropriate adaptation strategies and enhance food security globally.

The main elements of the project are:

• Inter-comparing crop models to reduce uncertainties in temperature, precipitation and greenhouse gas emissions
• Juxtapose with observations of temperature, precipitation and GHG emissions
• Developing stronger computer code and strengthening understanding of relationships within the system
• Calibration of model based on region
• Simulation impacts of soil health and water sources on agriculture
• Utilising models to develop suitable adaptation strategies

The crops tested within the context of AgMIP were originally wheat, maize, rice and sugarcane, with expansion now allowing to modelling of potato, sorghum-millet, peanut (groundnut) and soybean.

Source: www.agmip.org

MACSUR

MACSUR take a similar approach to AgMIP with their CropM project, which aims to improve the quality of crop models by reducing uncertainty through inter-comparison, scaling methodologies, data compilation, and inclusion of (often neglected) factors, such as, impact of climate change on flowering and the use of crop rotation modelling. By linking crop and soil modelling, they are able to obtain a stronger, more accurate depiction of reality, which will help them develop stronger adaptation and mitigation strategies to climate change, and well as strengthen global food security.

Source: www.hoffmann.wf

Livestock Production: Part 3 – A Modeller’s Perspective

It’s never easy explaining to anybody what you do at university, regardless of what you study. When I studied Mathematics for my bachelor's degree, people would always ask what that entailed, and would then proceed to get incredibly confused or uninterested when I tried to explain. This has lessened since studying Environmental Modelling, probably because the environment is something that almost all of us can identify with, or at least respect its importance (except maybe Donald Trump and the Tories). However, I still get asked the same question all the time: “what actually is environmental modelling?”

Source: http://memecrunch.com/

A model, in general, is a simulation of a real-life system for the purpose of understanding that system better. For example, you can use models to forecast future behaviour (without having to wait for time to unfold), and develop projections based on changes in the system (without having to tamper with the real-life system). However, to do this first requires assumptions about our system (parameters), boundary and initial conditions, and an understanding of the model and inter-connected relationships within it. Then, we can construct a model using field observation, laboratory experiments, mathematical equations, statistics, logic, and computation to best predict the system in the simplest way necessary.

In an environmental modelling context, these systems are highly variable. The whole Earth as a system is used for many different models, such as weather forecasting, fluctuations in sea surface temperatures and climate change. Alternatively, some models can focus on specific localised regions, like ecological models/population dynamics, hydrological systems, etc. We can even use models to simulate livestock systems!

But, why should we do that? Well, instead of using trial and error on the parameters of the model to increase productivity or predict environmental damage, models allow us to use computers, which is more cost-effective, faster and can avoid unnecessary environmental damage. However, predicting systems is incredibly difficult, which can sometimes make environmental models fairly unreliable. To avoid them going pear-shaped, we need to highlight the most vital parameters in our model and portray them as accurately as possible.

Source: http://www.cliparthut.com/

Here are some examples of livestock production models:

Global Livestock Environmental Assessment Model (GLEAM)

This model, produced by the FAO, does exactly what it says on the tin: assesses the environmental impacts of global livestock production, which include greenhouse gas emissions, land use, land degradation and forestry, feed, water use and species interaction, as well as determine possible adaptation and mitigation strategies on a regional and global scale.

The main elements of the model are as follows:

• Systematic, global coverage of meat and milk from cattle, sheep, goat and buffalo, meat and eggs from chicken, and meat from pigs.
• Spatial distribution models of livestock, climate data and feed yields to identify driving factors, environmental consequences and mitigation strategies
• Approximations of carbon dioxide, methane and nitrous oxide (GHG emissions) at each stage of production, and from fermentation and manure management

The outputs of the model are:

• ·    Spatial distribution and quantity of cattle, sheep, goats, buffaloes, chickens and pigs
•      Amount and management of manure
•      Volume, composition and quality of feed
•      Emissions emitted at each stage of production
•      Commodities produced by livestock

Integrated Model to Assess the Global Environment (IMAGE)

This model has been developed by the PBL Netherlands Environmental Assessment Agency to measure the environmental impacts of global human activity, including livestock production systems. This aspect of IMAGE is similar to GLEAM as it measures the impact of livestock production on greenhouse gas emissions, air and water pollutants, and land use.

European Livestock Policy Evaluation Network (ELPEN)

The purpose of ELPEN is to measure or predict the outcomes of policy change in the livestock industry. The model incorporates bio-geographical data (including climate, land cover, soil, water, topography), statistical data, economic, social, environmental and technical features of livestock systems, to produce conclusions based on social, economic and environmental effects of policy change.

Livestock Development Planning System, Version 2 (LDPS2)

The LDPS2 is a computer model to aid livestock development planners in decision-making for livestock systems, including those of cattle, sheep, goats, buffaloes, pigs and poultry.

The main features are:

• Calculating the size, composition and feed of livestock herds needed for a given demand of livestock products, including meat, milk, eggs, skin, wool, manure and power supply
• Measuring the growth of herds to find the “growth constraint” to meet demand
• Compares these estimates with the amount of resources available

Source: http://images.all-free-download.com/