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The identification of national agricultural and SDG constraints with SDGToolkit
Part 3

John Penrose,
International Development Correspondent,

SDGToolkit's Integrated Development Environment

This article will describe some of the analytical tools (ATs) that support the first step in the Due Diligence Design procedure (3DP) of the cloud-based software-as-a-service (SaaS) SDGToolkit system.

If you have not already done so it is worth reading the previous parts 1 and 2 of this review series.
Links are here:Part 1Part 2

In this article I proceed to use some of the analytical tools (ATs) available in the Global Constraints Analysis (GCA) module which is the first step in the SDGToolkit due diligence design procedures (3DP).

The volume of output of useful information of each tool is significant in terms of narratives, summary and detailed tabulations and graphs. In terms of A4 MS Word-equivalent I generated about 40 pages of reports and additional simulations added another 20 pages. Therefore the number of potential screen shots to show is very large so I reduced these to focus the attention of readers on the main output components.

Agenda 2030 has no SDG-linked common agricultural sector or project reference models so there are no agreed integrated analysis approaches for national and project planning requirements. Those that exist are not always easy to follow within a forest of SDG indicators. Having completed this review I think it demonstrates the degree to which the SDGToolkit's Integrated Development Environment remedies this problem. It combines the Global Constraints Analysis (GCA) module, dealing with national gaps with the subsequent procedures that focus on project design (to be reviewed in a forthcoming article). The GCA establishes the national context in terms of gaps and needs providing clearly identified priority project objectives and thereby aligning national and project efforts to common objectives.

Review of some capabilities of the Global Constraints Analysis (GCA)

The GCA provides analytical models (ATs) to review national constraints, gaps and needs.
GCA Route Map

SEEL has developed "GCA Route Map". This guides users on which particular sequence of ATs to follow to generate specific sets of desired results. This is undergoing an update as a result of new ATs being added. When this is released I will post a review.
All SDGToolkit output
is downloadable
in MS Office formats

The GCA can be used in many ways (see box on right). For example to conduct single analyses to confirm some existing data or to generate data concerning some matter of potential concern completing ad hoc spot checks. Any such series of procedures are classified as being sequentially incoherent because there is no organic linkage between the result other than they are based on the same algorithms but different inputs. Where a sequence followed is anchored in data inputs from a single country then the procedures and their output are classified as being sequentially coherent. Sequentially coherent analyses take longer but demonstrate the power of a stepwise logical sequence of procedures, each consisting of relatively simple, sometimes complex, analyses to end up with useful information for project designers in a specific country. The system provides a practical basis for more direct collaboration between government agencies, project teams and their stakeholders.

To save time I did not follow a sequentially coherent pathway but followed a specific path through a series of ad hoc analyses. My objective was to show typical outputs of each AT. This might disappoint some readers, but a sequentially coherent sequence requires a lot more detailed explanations at each step. Future reviews will include shorter GCA coherent sequences associated with project level analyses to demonstrate the power and effectiveness of this approach.

The sequence followed in this review is set out below:

  1. Generate population projections separating out younger cohorts

  2. Create commodity balance sheets to find out the degree of national self-sufficiency in needed commodities

  3. Calculate per capita food consumption levels to identify and measure the size of deficits

  4. Based on the above, project the future national requirements for specific foods linked to required target per capita consumption levels

  5. Work out the areas of land required to achieve different levels of self-sufficiency for critical commodities

  6. Complete a general scoping of the relationship between inflation and real income projections

  7. Determine the projections of real incomes or purchasing power of middle and lower income segments for food items

  8. Work out the minimum prices that provide farmers with a compensatory profit

  9. Identify policy provision options, if needed, to align the purchasing power of lower income segments to unit prices that sustain viable agricultural production of critical commodities

  10. Dimension the national priority gaps and needs in terms of the required programme size

I was surprised that with the onboard system's guidance, I was able to complete all of these analyses within a working day as an individual, in spite of never having worked on these specific topics before. Having worked in online systems for agriculture now for over 20 years I have not come across such a large collection of useful analytical tools (ATs) within a single integrated service.

SEEL's Analytical Tools Development Centre (ATDC) who produce SDGToolkit ATs, maintain a benchmark series of estimates of the costs of conventional team work in completing the tasks carried out by each analytical tool. They compared my reports, including optional analyses (scenarios), that I produced in a day to the their benchmarks series containing the current international market for specialized consultancy assignments. If the results had been a coherent sequence, anchored on data from a single country, what I produced had a valuation, in conventional terms of between $25,000 and $50,000. However, to my initial surprise, the valuation, based on the actual sequentially incoherent route I followed, was indeterminate. However I was impressed by that estimate of the worth if I had followed a coherent sequence of ATs. Even at the lower valuation for coherent sequences, this makes the cost-effectiveness of the GCA potentially outstanding. Expensive visit-based consultancy and technical support was not only eclipsed by Covid-19 but in the future SDGToolkit's remote delivery of digital intelligent solutions is likely to provide the preferred solution for policy makers, development organizations, governments and project teams in low income countries.

A caution on the issue of quality

Angus Raeburn of the ATDC advised me that they are adjusting the benchmarks based on assessments on comparisons of fee rates with quality of work which in the case of consultancy assignments is surprisingly variable. In many cases this is the result of poorly drafted terms of reference (ToR) and variations in the interpretation of ToRs by consultants. Often specifications are defined by the consultants which places government officials and donors in a weak position with consultants doing "what they know how to do" as opposed to "doing what is required". This remains a significant operational issue with governments sometimes receiving poorly developed strategies. As a result the fee rate benchmarks need to be weighted against quality of output. The advantage of the GCA output is that it is of a constant high quality in terms of the analyses undertaken so it provides government departments and donors with more leverage to specify ToRs based on a selection of SDGToolkit procedures or all of them. Then the issue becomes assessments of the quality of data used as input. This is a reason SEEL has invested a lot of effort in reducing these risks. Solutions include the use of Data Reference Models to specify dataset requirements and farm typologies to ensure farm surveys contain the required data. In the context of SDGs this is a really important topic and I will come back to this in another article on this specific issue.

Final observation

The normal approach to using the GCA is to secure an estimate of the number and "orders of magnitude" of the main national gaps and needs in a "reconnaissance" mode. Use is made of available information to run through a sequence of ATs fairly rapidly to detect obvious gaps e.g. excessive reliance on imports for a critical food commodity, excessive income disparities or deficient per capita consumption of essential foods for sustainable health. This is then followed by a reiterative mode of rerunning AT analyses to obtain credible and validated output. This process helps users to "get to know" the "territory" they are dealing with. This is of importance to project teams so they begin to formulate and record the types of solutions that might address the gaps registered. The more AT simulation options are repeated the understanding of the dimensions of gaps become better appreciated and some notions of prioritization in terms of the relative importance of gaps can be formed. Because these calculations are based on specific calculations or "models" there is seldom any room for disagreement on the significance of the analytical output.

Because of time constraints I have not followed this line of analysis but have simply completed each AT to demonstrate their functionality.

Dialogs appearing in this article

In general, some dialogs presented may differ from future versions because of SDGToolkit's proactive review and improvement policy. In fact, during the course of this evaluation three new ATs were added and one, which had become redundant was removed. This changed menu lists. This continuing process maintains backwards compatibility but is applied to all 3DP modules to advance the utility of the overall SDGToolkit provision. This process is based on feedback from stakeholders and the cumulative applied agricultural project experience of the SDGToolkit team.

Accessing the GCA

To get to the GCA dialog page and menu there is a double security check operated by the Plasma.Systems operating system. Users need to be authorized by their organizations to access the system. One security check (name and PIN) controlled by the server permits access to the platform and a second one (name and another PIN) controlled by the software modules, checks a user's access rights to change any data linked to a project. Where a specific project is not identified then all ATs can be accessed and results identified by specific autogenerated IDs.

The initial systems menu is shown below. The menus link to ATs which users can input data to generate projections, narratives, graphics and tabulations. By altering data inputs other optional information series can be generated. All options are retained for later reference in a central Project Memory and all options can be downloaded in MS Office document and presentation formats.

Menu used to access modules to add data

All modules and ATs come with on board guidance to orientate the user on how to use the system and the types of information required. I used this during this review of the GCA. However, no matter what phase of the project cycle the guidance system provides appropriate support.

The essential roles of analysis, propositions & advocacy

Carrying out adequate constraints analysis helps project teams estimate impacts and associated costs of current policies and to therefore estimate the benefits of changing policies. This information can be used to prepare propositions for policy changes or initiatives to raise the feasibility of addressing SDGs. Therefore the GCA approach is a fertile ground for the preparation of responsible positive and constructive advocacy.

The GCA layout

  1. Projections for population, output requirements and resources consumption

  2. Analysis of availability of commodity complexes

  3. Economics of unit prices, purchasing power, real incomes and producer margins

  4. Dimensioning of orders of magnitude of target objectives

  5. Sustainability projection tools: carrying capacity

  6. Climate impact projection tools: GHG emissions

This is a relatively advanced set of constraints and policy analysis tools some of which can produce output which would interface closely with typical policy analysis documentation content of governments and development organizations. Indeed, from records of internal workshops, comments from one policy analysis suggested that the GCA has a more complete set of analyses than those commonly applied by government planning departments or international development organizations. However, this is because the required level of detail on agricultural issues is greater. This enables users to identify and measure the impacts of existing policies on gaps and needs. For example, are gaps generated by policy, natural resources, economic/market factors, or all of them? In terms of needs, do existing policies impede solutions or support them?

The entry menu to the GCA is shown below.

Currently, the GCA AT library contains 25 ATs and the following stages of analysis in the 3DP at the project level includes some 55 ATs.

Screen shot of GCA menu

Above, I listed 10 analyses that will be demonstrated in this article. The first was:


This section starts off by projecting population numbers based on available official data. So by clicking on the access menu button the following menu selection appears:

The first menu item accesses a tool that calculates population growth based on official growth rates. The second tool completes the same projection based on birth and death rates but also adds estimates of the numbers born from the start date and their percentage participation of this cohort in the total population by advancing age group. This provides a profile of the changing requirements according to age cohorts. To secure a more complete cohort makeup as from a start date simply start the projection further back in time. Usually population numbers associated with earlier start date are available but there is a need to check on birth and death rates in the intervening periods before running the projection.

Example of population projections with cohorts is shown below.

This particular tool is used to generate baseline data for a wide range of associated analytical tools that cover economics, resource requirements, carrying capacity, food availability and real incomes. The example is for the United Kingdom.

The example is a single projection based on specific assumptions but any number of options can be generated. The data input screen is simple and easy to follow, as shown below.

On the right of the input dialog there is a guideline button. These buttons occur on all analytical tools and provide a guidance on the function of the tool as well as significance of the results and why these calculations are carried out. SDGToolkit has invested a lot of effort in the production of informative and useful contextual help; this helps users understand the processes involved, and the reasons for them, providing an underlying confidence in the process and credibility of results.

The first table is the data input and a summary of some results.

The system generates an automatic narrative to ensure a correct interpretation of results as shown below. This particular analytical tool is very simple so the narrative is likewise. However in some of the more advanced tools the narrative is an essential support for users where the analyses and results are more complex. The operational logic for the more advanced tools is based on advance decision analysis logic or AI.

The full dataset generated is provided below where the growth in the younger cohort features in the data in the four right hand columns.

The associated graphic output is shown below. The cohort data, depending upon the input data, provides important information in the fields of provisions for pre-natal services, child nutrition, educational requirements and many other life stage associations requiring the support of a sound local economy and access to services and essential products. This type of data is of importance in low income countries. However, the nature of economic "growth", in the United Kingdom has witnessed something like 35% of the population and their children enter into poverty status. The questions of poverty are addressed in other analytical tools that make use of baseline population and cohort data generated by this analytical tool.


In the further development of this article I will present the outputs of named analytical tools in order to reduce the amount of content. In all cases the input dialogs are simple and guidance is also good so a user remains "in control" of the sequence of analyses, understands what the tool is doing and why.

All projections end up with unique IDs so they can be recalled later. The main purpose of the initial run through GCA procedures is to secure a "orders of magnitude" of constraints, their impacts in terms of gaps as well as identifying likely primary solutions. Once procedures have been completed users will have gathered a good profile of the levels of importance of different gaps identified and then the analysis can be refined to come up with more precise measures of gaps and needs.

In terms of the production of key food commodities, item 5 in the GCA is "National commodity balance sheets" which generate projections on the national production, inventory and trade balance of many commodities. The image below is a typical output. This tool provides useful insights into the degrees of national self-sufficiency in production. For example in the example shown below there is a significant production deficit indicated by the large negative trade balances in grain and flour/meal. Addition data generated in this projection tabulates the availability in terms of availability per capita in comparison with the nutritional requirements. Commodity losses in harvesting, grain and flour store are calculated which can provide some justification for better harvesting technique whereas storage losses arise from pest infestation as well as the drying out of stored produce resulting in loss of weight.

The balance sheet tools cover grain, vegetables, fruits, oilseeds, orchard crops, roots, vines, wine, meat, milk and eggs. Each tool provides the options available within each commodity complex.


In the data input dialog there is a field requiring a per capita consumption figure in kg./annum/per capita. Here you either put the reported level or the desired level according to nutritional standards. The output of the Balance Sheet AT show the difference between this value and the value calculated on the basis of the balance sheet estimate of availability divided by the population. Naturally average figures are insensitive to acute problems of food intake and quality amongst lower income segments. This issue is handled in the analyses completed in items 6, 7 and 8.


In the context of commodity balance sheets and based on a national population growth and selected cohort analysis, it is possible to calculate a series of important analyses linked to nutrition, the need for certain types of food, what national land resources are required to produce the commodity through import substitution to alleviate deficits in per capita consumption. This is followed by more refined analyses determining the real incomes or purchasing power of population segments and the relationship between affordable prices for consumers and feasible prices for producers. Below is a typical output for this type of analysis.


The areas of agricultural land required to increase domestic production can be calculated making use of the GCA item 4. The screen shot below show an output of estimates of areas of land required to produce 250,000 additional tonnes of corn, according to different levels of productivity. Each level of productivity, in this case rainfed crops, i s associated with a specific "technical package" of inputs and production system each with different carbon footprints. The yields, it should be noted vary with season also (dependency on temperatures and water deficit). This means that the high yield option only attains those yields in "good seasons". Therefore projections need to be based on averages on the production system used in the knowledge that temperatures and water deficits are rising.


General impact of inflation on real incomes


This brings us to analyses which begin to focus attention on potential crisis points not only at national level but down to community levels associated with real income levels. No doubt most have considered the subject of living incomes which has been reviewed in an article on this site in the series: "Economic Policies for Agenda 2030", entitled,

"Living Income - this should be a critical object of macroeconomic policy"

Here the quality of data input is crucial but then so are the dynamic factors linked to family size and projections of real incomes into the future where corrections need to be made based on changes in prices. The easy way out is to use the so-called Consumer Price Index (CPI) which are averages projected across a nation. In reality consumer prices vary significantly across nations and are quite location specific. Smaller communities using products transported from other locations often face higher unit prices and often different rates of inflation than say a town dweller. According to SEEL, CPI data, for many rural communities, usually underestimates inflation, sometime by a significant margin. It is worth mentioning that local farmers also face variable input price inflation, ending up with a "terms of trade" (the relative movements in input and output prices) that work against agriculture.

When attempting to sort out anything to do with an income that affords basic essentials on a sustained basis, within the context of SDGs, it is essential to take into account production economics as well as family budgets to determine a viable pathway forward for producers and consumers.

This analytical tool makes use of family expenditure data making use of existing or surveys organized by a project team. To allow for cultural habits and differences in these types of datasets some field are marked as "additional items".

The inflation rates applied to the projection of family purchasing powers can include the CPI, which is likely to be the lowest and often unrealistic. Other rates should include recorded worst state and average inflation rates. In reality carrying out local surveys over time will generate better quality estimates of actual inflation rates.

The analysis generates data for average family income levels and the lower segment family incomes as shown below.

Average family income - impact of inflation on purchasing power

Lower family income - impact of inflation on purchasing power


Reviewing the trajectory of family disposable incomes spent on food items there is an accompanying trajectory of unit prices that can maintain the real purchases of food. In other words the family can at least consume the same physical quantities of food as at the start of a time series. However, this is only possible if the farm gate prices accompany or fall below this trajectory. Therefore depending upon the trajectory of farm variable input prices farmers will be able to maintain gross margins, or they can be driven to a loss. This would result, normally, in such output being diverted to higher income consumers or even to exports.

The decision of the feasibility of matching farm gate prices to an in market feasible price for lower income families depends upon the size of gross margin resulting and the decision of the farmer as to what is considered to be compensatory.

The left hand histograms show data base on average gross margin data while the right hand histograms allow for full seasonal variations associated with possible seasonal cycle variance in yields and embed risk factors.
Example 1: The feasible low income price range is not viable for farmers

Example 2: The feasible low income price range is viable for farmers


This last section is not really part of the normal concern for project teams but it is included to explain how much of the output of the GCA can provide valuable feedback to policy makers so as to assist them identify policies to ensure that funded projects end up achieving their objectives. Naturally this is of interest to project teams who do not want to design projects that will fail as a result of inappropriate policies. Teams should wish to avoid they types of impacts on low income consumers because that occur when, as often happens, when real income purchasing power dips, farmers will sell to higher income families. Often commodity dealers will purchase production for export or speculative hoarding to drive prices higher. Therefore, in order to satisfy the objective of helping lower income segment project stakeholders, a policy solution in terms of supportive measures is required.

In this case I focused on low income segments to quantify the extent of the national problem of poverty and their precarious position with respect to future ability to purchase food. The other analyses would have indicated if there was enough land to import-substitute and the pricing information indicates whether or not the farmers can compete with imports.

In terms of policy provisions, in the case of very low income families and farmers only having a marginal ability purchase and produce at desired prices, there is a need to review policy options to help sustain the viability of both.

The more significant factor is that these policy questions cannot in fact be resolved at this level of analysis. This is because the subsequent procedures in the 3DP conduct a more detailed evaluation of project prospects based on the conditions of localities where projects are considered to be located and based on 55 analytical procedures. In this sequence the additional constraints, or lack of them, in different locations where projects are expected to operate can add further details to orientate policy constituent targeting. For example, although production can occur in specific bioclimatic conditions in a specific area of a country, the constraints imposed by the terrain conditions and associated bioclimate in another area in the same country can alter the production potential and attainable gross margins of producers.

Climate change and the transition towards increasing Temperature and Water Stress conditions (TWS) is leading to falling attainable yields and therefore, in these circumstances, the "production functions" of the farms, used in project plans, need to change by including, for example, changes to the "production systems" including such techniques as water conservation and scheduling substitutions of crop varieties (genotypes) based on the meteorological cycles around the rolling means of temperature and rainfall. This locational-state genotypic sequencing (LSGS), developed by SEEL, is a way to lower the risk to yields as TWS conditions advance. Clearly this level of detail can only be collected during the project design stages to identify feasible project plans and costings. The ability to identify quite different production circumstances has an important contribution to make to the fine tuning of policy initiatives.


Dimensioning the size of national gaps and needs is of fundamental importance in assessing budgetary and investment requirements to bring about necessary change to address SDGs on a viable basis. The opportunity costs of inaction can be estimated and as a result the benefits of appropriate investment can also be estimated. By weighting investment requirements against the combination of farm production functions each with different input-output relationships provide the ability to weight investment requirements in a more realistic fashion. Therefore, as in the case of policy initiative analysis the establishment of multiproject initiatives as funded programmes depend on the information generated in the remaining 3DP procedures concerned with specific project constraints.
Why are nexus points important?

A nexus point is an important connection between different factors that determine the output of a model. If data is not coherent (related) nexus points disappear or risk creating erroneous correlative relationships.

All ATs are based on decision analysis models or determinant relationships of cause and effect, where the value of inputs determine the value of output based on a functional relationship or algorithm. In the GCA the correct way to run the sequence is to relate all data to a single country. As a result all of the data becomes coherent and relates to the substantive realities "on the ground" in the country concerned.

An important conclusion of the evidence-based work conducted by the OQSI between 2010-2020 on project performance, is that a significant reason for project failure is lack of coherence between macro and micro dimensions resulting from:
  • a lack of direct communication
  • a lack of shared approaches to analysis
In the case of the SDG environment where aligning national needs with project objectives, ensuring sequential coherence helps reduce this element of risk.

Summary of my performance

The volume of useful output generated within about 6 hours of work was impressive.

Normal error trapping works in the dialogs preventing decimals being entered where rounded figures are requested or rejecting text in numeric fields. Each dialog must be correctly completed otherwise you go nowhere.

SEEL went through my output based using the Real Time Monitoring and Evaluation (RTME) system which regenerates all of the analyses I completed. In general they gave me a reasonable mark for completing this work in the time with no previous training and just using their onboard guidance. They did question what they considered to be some unrealistic input figures (maybe some readers noticed these?).

However, they were disappointed in the fact that I had not completed a sequentially coherent series since, as far as they are concerned, this is one of the strengths of the GCA. The fact that I had frequently changed the target countries or did not specify a country, meant that I had amassed an incoherent dataset. As a result some important correlations or "nexus" points were lost (see box on right). Nexus points are essential glue in the macro analysis as well as in determining useful policy actions to ensure a sustainable operation and outcome of a multiproject initiative or a single project.

I found this slightly frustrating because the guidelines explain the importance of anchoring any sequence on a single country but I overlooked this important point. SEEL put this down to lack of familiarity with the system as a result of not having received any training on the system. In reality, SEEL had offered me a free short course but I had not taken this up. If I had, I am sure I would have realized the importance of demonstrating this important aspect and benefit of the GCA.

As a result I placed some emphasis in the intro to this review on the importance of sequential coherence. I have also agreed that when I review the project level ATs I will associate these with a coherent sequence from the GCA to demonstrate the power of this concept.

Posted: 20210811
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